Fire Ecology of Rangeland Arthropods in the Southern Great Plains by Britt Windsor Smith, M.S. a Dissertation in Wildlife, Aquat

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Fire Ecology of Rangeland Arthropods in the Southern Great Plains

Britt Windsor Smith, M.S.

A Dissertation

Wildlife, Aquatic, and Wildland Science and Management

Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

Approved by

Robin Verble, Ph.D. Chair of Committee

Brad Dabbert, Ph.D.

Richard Stevens, Ph.D.

Scott Longing, Ph.D.

Mark Sheridan Dean of the Graduate School

May, 2018

Acknowledgments

I would first and foremost like to thank my family: my wife Jessica

Miesner, my mother Angela Miles, my father Russell Smith, and my brother

Connor Smith. I would also like to thank my late uncle and grandfather, Mark

Smith and Albert Smith. I am grateful for my in-laws, John and Susan Miesner for their love and support.

I would like to particularly thank my advisor Dr. Robin Verble for her support and encouragement through this whole process. I wish to thank my lab mates Neil Estes, Jonathan Knudsen, and Heather Williams for their support and collaborative expressions of displeasure.

I wish to thank the Natural Resources Management, and particularly Dr.

Mark Wallace, for support through the process. Also, I would like to thank the

Texas Tech University Graduate College for financial support.

I also thank my office mates through the years for dealing with me.

Texas Tech University, Britt Smith, May 2018

Table of Contents

Acknowledgments ...... ii

Abstract ...... vii

List of Tables...... viii

List of Figures ...... x

I. Introduction ...... 1

Literature Cited ...... 5

II. Ground-Active and Plant-Dwelling Arthropod Communities

Response to Dormant-Season Fire in Mixed-Grass Mesquite

Rangelands of Texas, USA ...... 10

Abstract ...... 10

Introduction ...... 11

Materials and Methods ...... 13

Study Site...... 13

Environmental Variables ...... 15

Analysis ...... 17

Results ...... 19

Discussion ...... 21

iii

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Literature Cited ...... 25

Tables and Figures ...... 29

III. Northern Bobwhite Chick Prey Responses to Rangeland Fire.

...... 46

Abstract ...... 46

Introduction ...... 46

Methods ...... 49

Study Area ...... 49

Environmental Variables ...... 51

Arthropod Sampling ...... 52

Analysis ...... 53

Results ...... 54

Discussion ...... 56

Management Implications...... 59

Literature Cited ...... 60

Tables and Figures ...... 66

IV. Plant-dwelling Orthoptera Response to Prescribed Burning in the

Texas Rolling Plains...... 73

Abstract ...... 73

Introduction ...... 74 iv

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Materials and Methods ...... 77

Study Sites ...... 77

Grasshopper Sampling ...... 78

Analysis ...... 79

Results ...... 80

Discussion ...... 81

Literature Cited ...... 84

Tables and Figures ...... 87

V. Prescribed Fire Effects on Rangeland Dung Beetles in the Southern

Great Plains (Coleoptera: Scarabaeinae, Aphodiinae) ...... 94

Abstract ...... 94

Introduction ...... 94

Methods ...... 98

Study Area ...... 98

Trapping Methods ...... 99

Vegetation Structure Estimation ...... 100

Cattle Dung Density Estimation ...... 101

Analysis ...... 101

Results ...... 102

Discussion ...... 103

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Implications ...... 106

Literature Cited ...... 106

Tables and Figures ...... 112

VI. Post-Fire Summer Soil Surface Temperature in the Texas High

Plains...... 117

Abstract ...... 117

Introduction ...... 118

Methods ...... 121

Study Area ...... 121

Sampling Methods ...... 122

Analysis ...... 123

Results ...... 125

Discussion ...... 126

Literature Cited ...... 128

Tables and Figures ...... 131

Texas Tech University, Britt Smith, May 2018

Abstract

Fire has been a prominent disturbance in the American Great Plains since the end of the Pleistocene Epoch when humans first settled. Natural and anthropogenic fires maintained expansive grasslands and savannas that supported a diverse suite of fauna. In modern times, the reintroduction of fire onto rangelands of the Great Plains is a management goal that seeks to maintain these ecosystems by reducing the influence of woody plant species. Prescribed rangeland fire influences flora and fauna through direct and indirect effects.

Direct effects are typically mortality to living organisms. Indirect effects are changes to abiotic and biotic processes that influence organismal populations. My goal was to evaluate direct and indirect effects on arthropods located in mixed- grass mesquite rangelands of the Texas Rolling Plains. I evaluated ground-active and plant-dwelling arthropods response to dormant seasons prescribed fires. I did not observe a direct effect of dormant season prescribed fire on abundances of arthropod taxa. However, I did observe indirect influences of fire through changes in vegetation structure and cover on arthropod taxa abundances.

Further, I examined summer season long soil surface temperature differences among burned and unburned shortgrass rangelands. I found significantly greater variation in temperature extremes in recently burned areas compared to unburned areas. These results suggest that rangeland fire does affect arthropod populations in the Texas Rolling Plains. Further, land managers can use fire to influence arthropod populations that are important to wildlife species and that provide ecosystem services while also reducing woody plant abundance. vii

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List of Tables

Table 2.1 Relative abundance and counts for all identified

subclasses and orders...... 29

Table 2.2 Model results from treatment and

environmental covariates on ground-active

arthropod orders, families, and functional

feeding guilds...... 31

Table 2.3 Model results from treatment and

environmental covariates on plant-dwelling

arthropod orders, families, and functional

feeding guilds...... 32

Table 3.1. Arthropods positively selected by northern

bobwhite quail chicks. Selection affinity

determined based on selection relative to

other taxa described by author...... 66

Table 4.1 Relative abundance and counts for Orthopteran

species with counts greater than 5...... 87

Table 4.2 Model results from treatment and

environmental covariates on plant-dwelling

Orthoptera species...... 88

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Table 4.3 Model results from treatment and

environmental covariates on plant-dwelling

Orthopteran functional feeding guilds...... 89

Table 5.1. Results from model-based multivariate analysis

for environmental factors and covariates on

individual taxa...... 112

Table 6.1 Hourly soil surface temperature model predictor

variable coefficients and fit...... 131

Table 6.2 Mean daily soil surface temperature variance

model predictor variable coefficients and fit...... 132

Texas Tech University, Britt Smith, May 2018

List of Figures

Figure 2.1 Study site locations in the rolling plains ecoregion of

north-central Texas. Map data: Google, DigitalGlobe ...... 33

Figure 2.2 2014 sample sites located at Private Ranch 1. 100 m

vegetation and sweep net sample transects colored

yellow. 100 m pitfall trap arrays colored cyan...... 34

Figure 2.3 2014 and 2015 sampling sites at Private Ranch 2. 2014

sampling sites denoted by yellow vegetation and

sweepnet transects. 2015 sampling sites denoted by

pink vegetation and sweepnet transects. Map data:

Google, DigitalGlobe ...... 35

Figure 2.4 2015 sampling sites located at Matador Wildlife

Management Area. Sampling sites are represented by

pink vegetation and sweep net transects. Pitfall arrays

are excluded for clarity but are located between and

parallel to pink transects. Map data: Google,

DigitalGlobe ...... 36

Figure 2.5 Accumulation curves for ground-active families (a) and

plant-dwelling families (b) ± S.E...... 37

Figure 2.6 NMDS ordination of ground-active arthropods showing

orders and sites with fitted treatment groups and

environmental factors (burned and unburned) and x

Texas Tech University, Britt Smith, May 2018

covariates (vegetation visual obstruction [VO] and

percent bare soil). Burned (-0.003, -0.003) and

unburned (0.014, 0.001) factors share similar

coordinates and thus only burned is shown on this

ordination. Sites are coded by year with closed circles

representing 2014 and closed triangles representing

2015...... 38

Figure 2.7 NMDS ordination of ground-active arthropods showing

families and sites with fitted treatment groups and

environmental factors (burned and unburned) and

covariates (vegetation visual obstruction [VO] and

percent bare soil). Burned (0.003, -0.005) and

unburned (0.002, 0.006) factors share similar

coordinates and thus only burned is shown on this

ordination. Sites are coded by year with closed circles

representing 2014 and closed triangles representing

2015...... 39

Figure 2.8 NMDS ordination of ground-active arthropods showing

feeding guilds and sites with fitted treatment groups

and environmental factors (burned and unburned)

and covariates (vegetation visual obstruction [VO]

and percent bare soil). Burned (0.007, -0.004) and

unburned (0.002, 0.002) factors share similar xi

Texas Tech University, Britt Smith, May 2018

coordinates and thus only burned is shown on this

ordination. Sites are coded by year with closed circles

representing 2014 and closed triangles representing

2015...... 41

Figure 2.9 NMDS ordination of plant-dwelling arthropods

showing orders and sites with fitted treatment groups

and environmental factors (burned and unburned)

and covariates (vegetation visual obstruction [VO]

and percent live vegetation). Burned (-0.001, 0.003)

and unburned (-0.004, -0.002) factors share similar

coordinates and thus only burned is shown on this

ordination. Sites are coded by year with closed circles

representing 2014 and closed triangles representing

2015. Hym = Hymenoptera...... 42

Figure 2.10 NMDS ordination of plant-dwelling arthropods

showing families and sites with fitted treatment

groups and environmental factors (burned and

unburned) and covariates (vegetation visual

obstruction [VO] and percent live vegetation). Burned

(0.001, -0.001) and unburned (-0.001, -0.00) factors

share similar coordinates and thus only burned is

shown on this ordination. Sites are coded by year with

xii

Texas Tech University, Britt Smith, May 2018

closed circles representing 2014 and closed triangles

representing 2015...... 43

Figure 2.11 NMDS ordination of plant-dwelling arthropods

showing feeding guilds and sites with fitted treatment

groups and environmental factors (burned and

unburned) and covariates (vegetation visual

obstruction [VO] and percent live vegetation). Burned

(0.007, 0.005) and unburned (-0.004, 0.003) factors

share similar coordinates and thus only burned is

shown on this ordination. Sites are coded by year with

closed circles representing 2014 and closed triangles

representing 2015...... 45

Figure 3.1 NMDS ordination of ground-active arthropods caught

in pitfall traps showing families and sites with fitted

treatment groups and environmental factors and

covariates. Families are color coded by order...... 69

Figure 3.2 NMDS ordination of plant-dwelling arthropods

captured in sweep nets showing families and sites

with fitted treatment groups and environmental

factors and covariates. Families are color coded by

order...... 71

Figure 4.1 Species accumulation curve ± standard error...... 90

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Texas Tech University, Britt Smith, May 2018

Figure 4.2 NMDS ordination of plant-dwelling Orthoptera

showing species and sites with fitted treatment groups

and environmental factors (burned and unburned)

and covariates (vegetation visual obstruction [VO]

and percent live vegetation[LV]). Burned (0.001, -

0.012) and unburned (0.006, 0.013) factors share

similar coordinates and thus only unburned is shown

on this ordination. Sites are coded by year with closed

circles representing 2014 and closed triangles

representing 2015...... 91

Figure 4.2 NMDS ordination of plant-dwelling Orthoptera feeding

guilds and sites with fitted treatment groups and

environmental factors (burned and unburned) and

covariates (vegetation visual obstruction [VO] and

percent live vegetation). Burned (0.002, -0.001) and

unburned (-0.001,- 0.001) factors share similar

coordinates and thus only burned is shown on this

ordination. Sites are coded by year with closed circles

representing 2014 and closed triangles representing

2015...... 93

Figure 5.1. Dung beetle sample sites located at Matador Wildlife

Management area. Closed circles represent pitfall trap

locations and color indicates year where blue = 2015 xiv

Texas Tech University, Britt Smith, May 2018

and red = 2016. Red circles with blue stars are pitfall

trap sites that were sampled in both 2015 and 2016.

Prescribed burn areas are indicated by colored

polygons...... 113

Figure 5.2. Species accumulation curve with 95% confidence

interval...... 114

Figure 5.3. Environmental covariates compared between burn and

unburn treatment factors. a) Mean ± SE vegetation

visual obstruction (p = 0.097). b) Mean ± SE dung

density between treatments in sample sites (p =

0.308)...... 114

Figure 5.4. Comparison of log of Digitonthophagus gazella

individuals at sites to vegetation visual obstruction

with a fitted line ± SE. Fitted line: log(D. gazella) =

4.09 – 0.14(x) ...... 115

Figure 5.5. NMDS ordination showing species and sites with fitted

treatment groups and environmental factors and

covariates. Burned (-0.004, 0.002) and unburned

(0.004, -0.002) factors share similar coordinates and

thus only unburned is shown on this ordination. P.

difformis (D, -0.1, 0.07) shares similar coordinates as

Texas Tech University, Britt Smith, May 2018

B. ebenus (B, -0.08, 0.07) and is not shown. Sites

have been color coded by year...... 116

Figure 6.1 2017 false color image of sample sites located at PR1.

Wildfire boundaries outlined in orange. Location of

iButtons indicated by sample points with colored

circles. Sample pasture indicated by red polygon...... 133

Figure 6.2 Mean ± SE soil surface temperature methods between

burned and unburned areas at Lubbock Lake

Landmark. Methods include iButton, thermal infrared

gun (IR Gun), and soil temperature probe. Different

letters denote significance differences...... 134

Figure 6.3 Mean ± SE percent vegetation differences between year

of burn (YOB), one year post burn (1YPB), and

unburned wildfire treatments. Different letters denote

significance differences...... 135

Figure 6.4 Mean ± SE iButton daily soil surface temperature by

Julian date in year of burn (YOB), one year post burn

(1YPB), and unburned wildfire treatments. Points

have been offset to improve interpretability. Mean

daily solar radiation are denoted by closed grey

circles. Fitted regression lines from model parameters

Julian and treatment...... 136

xvi

Texas Tech University, Britt Smith, May 2018

Figure 6.5 Mean ± SE hourly iButton soil surface temperature by

hour in year of burn (YOB), one year post burn

(1YPB), and unburned wildfire treatments. Points

have been offset to improve interpretability. Fitted

regression lines from model parameters time, Fourier

terms, and burn treatment. Mean hourly ambient

temperature is represented by closed grey circles ...... 137

Figure 6.6 Mean ± SE daily variance of iButton soil surface

temperature by Julian date year of burn (YOB), one

year post burn (1YPB), and unburned wildfire

treatments. Points have been offset to improve

interpretability. Mean daily solar radiation

represented by closed grey circle...... 138

xvii

Texas Tech University, Britt Smith, May 2018

CHAPTER I

Introduction

Fire is a natural disturbance that consumes living and dead organic matter. In rangelands, dead organic matter is in the form of dead standing vegetation and decomposing vegetation on the soil surface (Wright and Bailey

1982). Through combustion, energy contained in the dead material is released in the form of heat (Wright and Bailey 1982). Living plant material is often consumed in fires, but the extent of damage and/or mortality depends on the residence time of the flame, heat intensity, and leaf moisture content (Wright and

Bailey 1982). Generally, post-fire rangeland ecosystems have increased bare ground. The exposed soil surface is typically warmer in the day due to increased solar exposure and cooler at night due to lack of insulating litter layer (Peet et al.

1975). Increased soil temperature stimulates plant growth when ample water is available (Peet et al. 1975). Reduced litter depth can have important consequences for rangeland plants and soil invertebrates. Litter reduces the evaporation of soil moisture (Wright and Bailey 1982) and limits the amount of inorganic nitrogen available for nitrogen-fixing bacteria on the soil surface

(Knapp and Seastedt 1986). Further, rangeland fires have been found to increase mineral phosphorus and light, which in turn increases nitrogen fixation by soil surface bacteria (Eisele et al. 1989). The resulting ecosystem shift can influence plant and animal communities in several different ways, which has implications for wildlife management. 1

Texas Tech University, Britt Smith, May 2018

Natural and anthropogenic fires occurred frequently on rangelands of the central United States from the end of the Pleistocene Epoch to early European settlement (Anderson 2006). In conjunction with animal grazing and climate, fires limited the abundance of woody plants in the Great Plains (Briggs et al.

2002). Natural fires in the Great Plains (i.e., lightning fires) typically occurred during the summer months, and were less frequent than anthropogenic fires

(Higgins 1986, Ewing and Engle 1988). Anthropogenic use of fire in Texas rangelands has historically varied with local culture and colonization; fires occurred frequently during habitation of early indigenous cultures but were suppressed and decreased in frequency during European settlement (Sauer 1950,

Humphrey 1953, Pyne 1982, Stambaugh et al. 2014).

Humans first arrived in North America around 15,000 y BCE from Asia by means of the Bering Strait (Goebel et al. 2008). During the end of the Pleistocene

(11,000 y BCE) humans began to settle in what is now considered the Great

Plains (Holliday 1985). During this period, pollen records indicate that the vegetation community of northwest Texas was dominated by coniferous species

(Wells 1970). As the Pleistocene glaciers retreated and drier conditions became more common due to a warming climate, the plant community shifted to more xeric species (Axelrod 1985). Humans brought fire usage to the Great Plains from

Asia and started fires due to intentional and accidental ignition (Sauer 1950,

Sauer 1961). Intentional ignitions by indigenous people served several purposes such as harvesting nuts, driving or attracting animals, clearing land, and reducing nuisance insects (Stewart 1951, Axelrod 1985). These human-ignited fires further

Texas Tech University, Britt Smith, May 2018 shifted the plant community in the Great Plains, as frequently occurring fires tend to limit woody plant species in favor of herbaceous grasses, forbs, and shrubs (Sauer 1950, Stewart 1951, Wright and Bailey 1982, Axelrod 1985); pollen records from central Texas bogs provide evidence for this shift (Bryant 1977). An increased occurrence of grass and forbs benefited small and large herbivores in the Great Plains (McNaughton 1984, Anderson 2006). In addition, the topography of the Great Plains, with generally flat, treeless, and dry expanses, frequently experienced strong sustained winds that were capable of carrying fires for long distances (Anderson 2006, Stambaugh et al. 2014). Fire use in conjunction with selective animal grazing created a patchwork of different vegetation structures whereby grazing animals selected recently burned areas to graze and avoided unburned areas (Fuhlendorf et al. 2008). This patchwork of burned areas at various spatial and temporal scales led to increase species diversity in the Great Plains (Wiens 1997). Anthropogenic use of fire in Texas rangelands has historically varied with local culture and colonization; fires occurred frequently and stochastically during habitation of early indigenous cultures but were suppressed and decreased in frequency during European settlement (Sauer 1950, Humphrey 1953, Pyne 1982, Stambaugh et al. 2014).

Many early European explorers recount fire use by indigenous Americans in the Great Plains (Stewart 1951, Pyne 1982, Stewart 2002, Stambaugh et al.

2014). By the nineteenth century, Europeans migrated onto the Great Plains, which resulted in the suppression of fire in most regions (Pyne 1982, Courtwright

2007). Fire suppression in the Great Plains has led to increased woody plant

Texas Tech University, Britt Smith, May 2018 abundance in rangelands, which reduces habitat quality for grassland obligate wildlife (Briske et al. 2006, Engle et al. 2008). More woody plants on historically grass and forb dominated rangelands causes habitat fragmentation that negatively impacts native wildlife populations (Coppedge et al. 2001, Horncastle et al. 2005). Cyclical use of fire reduces woody plant encroachment and promotes vegetation heterogeneity, which is beneficial for grassland-obligate wildlife populations (Briggs et al. 2002, Coppedge et al. 2008).

Encroachment and dominance of woody plants on rangelands can have several economic effects for landowners from reducing forage for cattle to lowering hunting lease values. Brushland and grassland are two of the most commonly leased hunting land in Texas (Adams et al. 1992). Texas landowners in Blanco and Gillespie counties consider 5-50% canopy cover to be an ideal cover for their rangelands (Krueter et al. 2004). Texas landowners identify several reasons for controlling brush and woody plants on Texas rangelands, primarily improving economic outcomes (Kreuter et al. 2005). Further, landowners perceive prescribed fire as a brush management tool that is both inexpensive and effective at reducing honey mesquite (Prosopis glandulosa) and prickly pear (Opuntia polyacantha) abundance (Kreuter et al. 2005). The use of prescribed fire to reduced woody plant abundance in mixed grass-mesquite rangelands has an estimated cost of $6 ha-1, compared to herbicide and mechanical at $42-62 ha-1 and $124-222 ha-1 respectively (Teague et al. 2001).

Texas landowners identify insufficient resources, insufficient knowledge, and legal concerns among the main reasons for not implementing prescribed fire on

Texas Tech University, Britt Smith, May 2018 their rangelands (Kreuter et al. 2008). Prescribed burn cooperatives are one solution to many of the problems to implementing the restoration of fire regimes on mixed grass-mesquite rangelands of Texas (Twidwell et al. 2013).

The use of prescribed fire has the ability to impact animal populations and communities through changes in vegetation structure, cover, and composition. In this project, I sought to examine the influence of prescribed fire and the resulting vegetation changes on arthropods in mixed-grass mesquite rangelands of north- central Texas. Arthropods are an important group of animals, as they are important food sources for many bird and mammal species, can cause severe economic loss to rangelands and croplands, and influence vegetation through biotic and abiotic pathways. Arthropods are a large taxonomic group and teasing out influences and patterns of particular taxa is a challenging endeavor. However, understanding the role wildland fire plays on this important suite of species is critical for making land management decisions that can cause beneficial or deleterious cascades through the greater ecosystem.

Literature Cited

Adams, C. E., Thomas, J. K., Ramsey, C. W., 1992. A synopsis of Texas hunting leases. Wildlife Society Bulletin 20, 188–197.

Anderson, R. C., 2006. Evolution and origin of the central grassland of North America: climate, fire, and mammalian grazers. Journal of the Torrey Botanical Society 133, 626–647.

Axelrod, D. I., 1985. Rise of the grassland biome, central North America. The Botanical Review 51, 163–201.

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Briggs, J. M., Knapp, A. K., Brock, B. L., 2002. Expansion of woody plants in tallgrass prairie: A fifteen-year study of fire and fire-grazing interactions. American Midland Naturalist 147, 287–294.

Briske, D. D., Fuhlendorf, S. D., Smeins, F. E., 2006. A unified framework for assessment and application of ecological thresholds. Rangeland Ecology & Management 59, 225–236.

Bryant, V. M., 1977. A 16,000 year pollen record of vegetational change in central Texas. Palynology 1, 143–156.

Coppedge, B. R., Engle, D. M., Masters, R. E., Gregory, M. S., 2001. Avian response to landscape change in fragmented southern Great Plains grasslands. Ecological Applications 11, 47–59.

Coppedge, B. R., Fuhlendorf, S. D., Harrell, W. C., Engle, D. M., 2008. Avian community response to vegetation and structural features in grasslands managed with fire and grazing. Biological Conservation 141, 1196–1203.

Courtwright, J., 2007. “When I first come here it all looked like prairie land almost”: Prairie fire and plains settlement. The Western Historical Quarterly 38, 157–179.

Eisele, K. A., Schimel, D. S., Kapustka, L. A., Parton, W. J., 1989. Effects of available P and N:P Ratios on non-symbiotic dinitrogen fixation in tallgrass prairie soils. Oecologia 79, 471–474.

Engle, D. M., Coppedge, B. R., Fuhlendorf, S. D., 2008. From the dust bowl to the green glacier: Human activity and environmental change in Great Plains grasslands. In Western North American Juniperus Communities (O. W. V. Auken, Editor). Ecological Studies 196, 253–271.

Ewing, A. L., Engle, D. M., 1988. Effects of late summer fire on tallgrass prairie microclimate and community composition. American Midland Naturalist 120, 212–223.

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Fuhlendorf, S. D., Archer, S. A., Smeins, F., Engle, D. M., Taylor, C. A., 2008. The combined influence of grazing, fire, and herbaceous productivity on tree– grass interactions. In Western North American Juniperus Communities (O. W. V. Auken, Editor). Ecological Studies 196, 219–238.

Goebel, T., Waters, M. R., O’Rourke, D. H., 2008. The late Pleistocene dispersal of modern humans in the Americas. Science 319, 1497–1502.

Higgins, K. F., 1986. Interpretation and compendium of historical fire accounts in the northern Great Plains. US Fish and Wildlife Service. No. 161, 0-39.

Holliday, V. T., 1985. Archaeological geology of the Lubbock Lake site, Southern High Plains of Texas. Geological Society of America Bulletin 96, 1483– 1492.

Horncastle, V. J., Hellgren, E. C., Mayer, P. M., Ganguli, A. C., Engle, D. M., Leslie, D. M., 2005. Implications of invasion by Juniperus virginiana on small mammals in the southern Great Plains. Journal of Mammalogy 86, 1144–1155.

Humphrey, R. R., 1953. The desert grassland, past and present. Journal of Range Management 6, 159–164.

Knapp, A. K., Seastedt, T. R., 1986. Detritus accumulation limits productivity of tallgrass prairie. BioScience 36, 662–668.

Kreuter, U. P., Tays, M. R., Conner, J. R., 2004. Landowner willingness to participate in a Texas brush reduction program. Rangeland Ecology & Management 57, 230–237.

Kreuter, U. P., Amestoy, H. E., Kothmann, M. M., Ueckert, D. N., McGinty, W. A., Cummings, S. R., 2005. The use of brush management methods: A Texas landowner survey. Rangeland ecology & management 58, 284–291.

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Kreuter, U. P., Woodard, J. B., Taylor, C. A., Teague, W. R., 2008. Perceptions of Texas landowners regarding fire and its use. Rangeland Ecology & Management 61, 456–464.

McNaughton, S. J., 1984. Grazing lawns: animals in herds, plant form, and coevolution. The American Naturalist 124, 863–886.

Peet, M., Anderson, R., Adams, M. S., 1975. Effect of fire on big bluestem production. American Midland Naturalist 94, 15–26.

Pyne, S. J., 1982. Fire in America. A cultural history of wildland and rural fire. Princeton University Press, Princeton, New Jersey.

Sauer, C. O., 1950. Grassland climax, fire, and man. Journal of Range Management 3, 16–21.

Sauer, C. O., 1961. Fire and early man. Paideuma 7, 399–407.

Stambaugh, M. C., Sparks, J. C., Abadir, E. R., 2014. Historical pyrogeography of Texas, USA. Fire Ecology 10, 72–89.

Stewart, O. C., 1951. Burning and natural vegetation in the United States. Geographical Review 41, 317–320.

Stewart, O. C., 2002. Forgotten fires: native americans and the transient wilderness. University of Oklahoma Press, Norman.

Twidwell, D., Rogers, W.E., Fuhlendorf, S.D., Wonkka, C.L., Engle, D.M., Weir, J.R., Kreuter, U.P., Taylor, C.A., 2013. The rising Great Plains fire campaign: citizens’ response to woody plant encroachment. Frontiers in Ecology and the Environment 11, 64–71.

Wiens, J. A., 1997. The emerging role of patchiness in conservation biology. In The ecological basis of conservation: heterogeneity, ecosystems, and biodiversity. (S. Pickett, R. S. Ostfeld, M. Shachak, G. Likens, Editors) Chapman & Hall, New York. pp. 93–107.

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Wells, P. V., 1970. Postglacial vegetational history of the Great Plains. American Association for the Advancement of Science. Science 167, 1574–82.

Wright, H. A., Bailey, A. W., 1982. Fire ecology: United States and southern Canada. In. John Wiley & Sons.

Texas Tech University, Britt Smith, May 2018

CHAPTER 2

Ground-Active and Plant-Dwelling Arthropod Communities

Response to Dormant-Season Fire in Mixed-Grass Mesquite

Rangelands of Texas, USA

Abstract

Terrestrial arthropods are a diverse taxon that have important positive benefits and negative costs in rangelands of North America. I sought to examine the influence of prescribed fire, a common rangeland management tool, on the arthropod community of mixed-grass mesquite dominated rangelands in north- central Texas. Dormant season fire was applied to 3 sites in 2014 and 9 sites in

2015, and I sampled ground-active and plant-dwelling arthropods using pitfall traps and sweep nets, respectively. I sampled arthropods from mid-July to early

August in both years. To analyze arthropod taxa, I used model-based multivariate methods and included treatment and vegetation covariates into my models. I further grouped arthropods into functional feeding guilds to examine whether fire had an influence. At the order level, I found no influence of fire on ground- active arthropods. I found an influence of percent live vegetation on the community structure, but no fire treatment effect on plant-dwelling arthropod orders. I did not find an influence of prescribed fire on either ground-active or plant-dwelling feeding guilds. These results suggest that the arthropod community, when examined several months post fire, have mostly recovered from the direct effects of prescribed fire. However, some families in the analysis 10

Texas Tech University, Britt Smith, May 2018 were influenced by fire altered vegetation structure and cover suggesting an indirect effect of prescribed fire.

Introduction

Prescribed rangeland fire is a common land management tool in the Great

Plains to improve livestock forage quality, reduce wildfire risk, and reduced woody plant abundance. The management of arthropods is another potential use of prescribed fire. Prescribed fire has been found to reduce the abundance of ticks following spring prescribed fire (Cully 1999). Further, pasture management that incorporates fire can reduce horn fly abundance, a common pest of cattle in the

Great Plains that can lead to reduced cattle weight gains (Scasta et al. 2012).

Arthropods are highly diverse taxonomic group that includes spiders, mites, insects, and centipedes among others. Arthropods have enormous positive and negative economic importance. Arthropods can damage crops, spread animal and plant diseases, and disrupt ecosystems (Speight et al. 2008). In contrast, they also pollinate crops, damage and consume pest arthropods, and improve fertility in rangelands (Speight et al. 2008). Examining the influence of prescribed fire on arthropod taxa can provide insights to further help manage rangelands of the

Great Plains.

Many studies have examined the direct and indirect influence of fire on arthropod communities and populations. Direct effects of wildland fire include direct mortality or damage of individuals, larvae, or eggs to fire (Curry 1994).

Indirect effects of wildland fire include such influences as changes in plant

Texas Tech University, Britt Smith, May 2018 composition, plant structure, temperature, available moisture, and other available resources (Curry 1994). Grassland insect responses to fire can also be broadly categorized as positive, negative, and/or neutral outcomes, depending upon species, location, habitat, and/or burn regime. Four phases of arthropod response have been identified: preburn, combustion, shock, and recovery

(Warren et al.1987).

To date, no studies have examined the influence of prescribed fire on the ground-active and plant-dwelling arthropod community in mixed-grass mesquite rangelands of the Texas Rolling Plains. In the Rolling Plains mixed-grass prairie of Oklahoma, a study examining patch burn grazing on arthropods found greater abundances of Araneae and Coleoptera on unburned pastures and greater abundance of Hemiptera on burned patches of patch burned grazed pastures

(Doxon et al. 2011a). The same study found no significant differences between patch burned pastures and unburned pastures for Diptera, Homoptera,

Hymenoptera, Lepidoptera, and Orthoptera (Doxon et al. 2011a). In another patch burn grazing study conducted in tallgrass prairie, invertebrate biomass was fifty percent greater in transitional patches within patch burn grazed pastures compared to unburned pastures, and transitional patches also had greater

Orthoptera and Hemiptera biomass (Engle et al. 2008). In tallgrass coastal prairie near Houston, Texas, annual fires were found to increase arthropod diversity due to changes in woody plant abundance, and that the fire regime that minimized woody plants also maximized arthropod diversity (Hartley et al.

Texas Tech University, Britt Smith, May 2018

2007). Burned tallgrass prairie in Kansas also housed greater total number of arthropods and greater biomass of arthropods (Nagel 1973).

My objective was to characterize the influence of prescribed fire on ground-active and plant-dwelling arthropods in mixed-grass mesquite rangelands of north central Texas. Previous studies have shown a mixed response of arthropods to rangeland fire. Araneae and Acari have been found to generally respond negatively to fire (Koerth et al. 1986, Engle et al. 2008, Doxon et al.

2011). Hemiptera, Hymenoptera, and Coleoptera have generally responded positively to rangeland fire (Rice 1932, Van Amburg et al. 1981, Winter 1984,

Anderson et al. 1989). Due to these previous results, I hypothesized that Araneae and Acari will have lower abundance and Hemiptera, Hymenoptera, and

Coleoptera will have greater abundance in recently burned areas of mixed-grass mesquite rangelands. Since arthropods are also influenced by vegetation structure and cover, I included the environmental covariates of percent bare soil and vegetation visual obstruction for models examining ground-active arthropods and percent live vegetation and vegetation visual obstruction models examining plant-dwelling arthropods.

Materials and Methods

Study Site

I conducted this study on 2 private ranches and a public wildlife management area located in the Rolling Plains region of north-central Texas (Fig.

2.1). Private Ranch 1 (PR1) was sampled in 2014 only and is located in Archer

Texas Tech University, Britt Smith, May 2018

County, Texas (33.57 N, 98.71 W; Fig. 2.2). Private Ranch 2 (PR2) was sampled in both 2014 and 2015 and is located in Dickens County, Texas (33.42 N, 100.88 W;

Fig. 2.3). Matador Wildlife Management Area (WMA), which is owned and operated by the Texas Parks and Wildlife Department, was sampled in 2015 and is located in Cottle County, Texas (34.11 N, 100.35 W; Fig. 2.4).

At PR1, I conducted 3 prescribed fires on the 11 April 2014. Prescribed burn units were 1-2 ha in size each. Thirty year mean annual precipitation for this site is 780 mm and 30 year mean annual temperature is 17.6˚C (NOAA-NCDC

2018). Precipitation in 2014 at PR1 was 487 mm. Soils located in each sample area range from fine sandy loam to clay loam (Soil Survey Staff 2018). Pastures at

PR1 are typically grazed with cattle, but in my study, cattle were deferred from study areas. The elevation at PR1 is 307 m.

In 2014, I opportunistically sampled a 12 ha wildfire that took place between 4-11 May 2014 at PR2. In 2015, 3 prescribed fires were conducted on 10

April (24, 33, and 50 ha in size). Thirty year mean annual precipitation and temperature are 577 mm and 16.2˚C, respectively (NOAA-NCDC 2018). Annual precipitation in 2014 and 2015 were 517 mm and 850 mm. Sampling areas were all dominantly clay loam soils (Soil Survey Staff 2018). The elevation at PR2 is

703 m.

In 2015, Texas Parks and Wildlife conduced 3 prescribed fires at Matador

WMA between 11-27 March 2015. These prescribed fires were 21, 372, and 2314 ha in size. Thirty year mean annual precipitation and temperature are 633 mm and 17.2˚C. In 2015 the annual precipitation was 803 mm. Soils in the sampling

Texas Tech University, Britt Smith, May 2018 areas at Matador WMA range from very fine sandy loam to loamy fine sand (Soil

Survey Staff 2018). The elevation at Matador WMA is 526 m.

I established plots in paired burned and unburned sites. I located study sites based on flat topography, accessibility, similar soil type, and distance to adjacent sampling sites. In 2014 I sampled 4 replicate treatments, and I sampled

9 replicate treatments in 2015. The closest 2 sampling sites were 440 m apart, all the others were greater than 1 km apart. Within each burned and unburned paired treatment site, I established one pitfall trap array consisting of 5 traps spaced 20m apart and two 100 m sweep net transects. In 2014 I conducted sampling from 25 July to 18 August, and in 2015, I sampled from 11-31 July.

Environmental Variables

To measure vegetation covariates, I established two 100 m transects parallel and 25 m to either side of the pitfall trap array at each sampling site. To sample vegetation cover, I placed a 1 m2 quadrate frame at each 10 m mark along the transect and estimated vegetation cover to the nearest 5% for bare soil, live vegetation, dead vegetation, and litter. To estimate vegetation structure, I used a modified Robel pole method, in which vegetation visual obstruction was measured to the nearest 1 cm on a Robel pole at a distance of 2 m from the pole and 1 m above the ground (Robel et al. 1970). Four measurements were taken for each cardinal direction every 10 m along the 100 m transects. I averaged the measurements along the two transects to give mean vegetation visual obstruction, percent bare soil, and percent live vegetation for each sampling site.

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I took vegetation samples between 4 – 5 August 2014 and between 29 June and

29 July 2015.

Arthropod Sampling

To sample ground-active arthropods, I used pitfall trap arrays consisting of 5 traps placed 20 m apart along a 100 m transect. Pitfall traps consisted of two

500 mL plastic cups filled with 100 mL water, 50 mL propylene glycol, and a drop of unscented dish detergent (Samways et al. 2010). Each pitfall trap was covered with a polystyrene plate anchored with sod staples to prevent rainfall from entering the trap (Woodcock 2005). I collected and stored trap contents in

532 mL Whirl-pak™ bags (Nasco, Fort Atkinson, WI). I collected and refilled pitfall traps every 3-5 days for six consecutive windows during the sampling period in both years.

To sample plant-dwelling arthropods, I sweep-netted along two 100 m transects 25 m away from and parallel to the pitfall trap array. I conducted sweeps by passing a 30.5 cm diameter sweep net through vegetation just above the soil surface in a 180-degree arc while walking along the transect. Every 50 m,

I emptied the contents of the sweep net into a 3.78 L resealable plastic bag, in which arthropods were euthanized with a cotton ball containing ethyl acetate.

Samples were then stored in an Idylis model ICM070LC freezer at -5 ˚C. I identified and counted individual adult arthropods to family level except for

Araneae, Collembola, and Acari which were identified at the subclass or order level, using readily available taxonomic keys (e.g., Borror and White 1998, Arnett

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2000). I also separately counted larval Lepidoptera because of their distinct functional ecology as compared to their adult forms.

Analysis

All analyses were conducted using the program R (R core team 2017). I examined each of the environmental covariates compared to the treatment factors using a Welch’s two sample t-test. Environmental variables were also compared for correlation using a Pearson correlation coefficient with the function rcorr in the package Hmisc in R.

I examined arthropods at the order and family levels. Arthropod subclasses were treated as order for analysis. I chose orders with a relative abundance of 1 percent or greater for analysis. Families used for analysis came from these orders with highest relative abundance. I examined pitfall trap and sweep net data separately due to the inherent sampling bias of each technique.

Pitfall traps that contained vertebrates, were depredated or were dry upon collecting were removed from analysis. Pitfall traps containing vertebrates had relatively high abundances of arthropods attracted to carrion and thus were removed from analysis. As a result of removing these pitfall traps from analysis, I calculated mean abundance for each taxon in pitfall traps for each treatment site rather than summing taxa counts for treatment sites. I also grouped orders and families with high relative abundance (greater than 1%) into functional feeding guilds to examine the influence of prescribed fire and my covariates on arthropod feeding strategies. Arthropods from orders and families were grouped into either carnivore, carrion-feeding, detritivore, fungivore, herbivore, nectarivore, sap-

Texas Tech University, Britt Smith, May 2018 feeding, seed-feeding, or a mixed strategy based on the dominant feeding ecology of adult arthropods in the order or family (Kral et al. 2017). Diptera and flying

Hymenoptera were removed from analyses as bycatch because they are highly mobile and more thoroughly sampled using other methods (Doxon et al. 2011b). I ensured my sampling was sufficient for analysis by plotting accumulation curves for ground-active and plant-dwelling arthropod families using randomly added sites with 100 permutations via the vegan package version 2.4-4 (Oksanen et al.

2017; Fig. 2.5).

To analyze the effect of fire arthropod orders and families I used recently developed model-based multivariate methods (Warton et al. 2015). These methods offer advantages over traditional distance-based methods such as accounting for the mean-variance relationship that exists in highly dimensional datasets and allows for the examination of model validation in relation to observed data rather than a derived distance matrix (Warton et al. 2015). I examined arthropod community count data with generalized linear models using a negative binomial distribution. Significance was determined using permutation tests with 999 resampling iterations. I also examined individual order, family, and feeding guild responses to treatment factors and environmental covariates using post-hoc univariate tests adjusted for multiple comparisons with step-down resampling. My analysis at the order level included 7 orders for ground-active arthropods and 5 orders for plant-dwelling arthropods. My analysis at the family level included 48 families for ground-active arthropods and 56 families for plant- dwelling arthropods. For ground-active arthropods caught in pitfall traps, I used

Texas Tech University, Britt Smith, May 2018 an additive model with burn treatment factors, percent vegetation visual obstruction, and percent bare soil as covariates. For plant-dwelling arthropods, I used an additive model with burn treatment factors, percent visual obstruction, and percent live vegetation as covariates. I conducted these analyses using the function anova.manyglm in the package mvabund version 3.12.3 in R (Wang et al. 2012). I verified model assumptions by examining the mean-variance relationship for a quadratic correlation and log-linearity assumption for a random pattern of residual values compared to fitted values. To visualize analyses, I created ordination plots using nonmetric multidimensional scaling

(NMDS) with Bray-Curtis Distances which were run with 2 dimensions. I created ordination plots using the vegan package in R (Oksanen et al. 2017).

Results

Pitfall traps contained a total of 247,985 individual arthropods from 23 orders, 2 subclasses (Collembola, Acari), and 1 class (Chilopoda, Table 2.1). When

I excluded rare (<1% total relative abundance) taxa, pitfall traps contained

243,979 individual arthropods from 5 orders and 2 subclasses. Collembola, excluding the order Symphypleona, was the most abundant taxa found in pitfall traps with 183,894 individuals, accounting for 74% of relative abundance. From sweep nets, I identified a total of 31,724 individual arthropods from 12 orders

(Table 2.1). Excluding rate taxa reduced total arthropod abundance to 29,732 individuals from 5 orders. Hemiptera was the most abundant taxon caught in sweep nets with 16,886 individuals, accounting for 53% relative abundance. The

Texas Tech University, Britt Smith, May 2018 most abundant family caught in sweep nets was Cicadellidae with 9,443 individuals.

Vegetation visual obstruction and percent bare soil (r = -0.67) and vegetation visual obstruction and percent live vegetation (r = 0.48) correlated at study sites. I also found a significantly greater percentage of bare soil in recently burned areas (x ̅ = 35.3) compared to unburned (x ̅ = 11.8, Welch’s t = 5.8, d.f. =

18.1, P < 0.001). I did not find a significant difference in percent live vegetation between treatments (Welch’s t = -0.007, d.f. = 19.9, P = 0.99) or in vegetation visual obstruction between treatments (Welch’s t= -1.67, d.f. = 20.6, P = 0.11).

For ground-active arthropods examined at the order level, I did not find a significant influence of my treatment factors or environmental covariates on the community as a whole or individual orders (Table 2.2). At the family level, I found a significant influence of vegetation visual obstruction on the assemblage of families, but I did not find an influence of treatment or percent bare soil (Table

2.2). I also did not find an influence of treatment or environmental covariates on individual families. Examining treatment and environmental covariates on ground-active functional feeding guilds, I did not find a significant influence on feeding guilds (Table 2.2). For individual feeding guilds, I found a significant positive influence of vegetation visual obstruction on sap-feeding arthropods (n =

1850, b = 0.11, dev = 9.63, P = 0.030). The NMDS results for ground-active orders, families, and feeding guilds show the relatedness of sites and species in ordination space with fitted environmental variables (Fig. 2.6, Fig. 2.7, Fig. 2.8)

Texas Tech University, Britt Smith, May 2018

Examining plant-dwelling arthropods at the order level, I found a significant influence of percent live vegetation on my assemblage of orders (Table

2.3). For individual orders, I found a significant positive influence of percent live vegetation on Orthoptera abundance (n = 1142, b = 0.05, dev = 11.63, P = 0.015).

At the family level, I found a significant influence of vegetation visual obstruction and percent live vegetation on assemblage of families, but no influence of treatment (Table 2.3). For individual families, I found a significant positive influence of percent live vegetation on the family Rhyparochromidae (n = 340, b

= 0.10, dev = 16.82, P = 0.050). When examining plant-dwelling functional feeding guilds I found a significant influence of percent live vegetation on feeding guilds (Table 2.3). For individual feeding guilds, percent live vegetation had a significant negative influence on sap-feeding plant-dwelling arthropods (n =

13,802, b = -0.2, dev = 11.32, P = 0.028) and positive influence on seed-feeding plant-dwelling arthropods (n = 561, b = 0.07, dev = 10.49, P = 0.030). The NMDS results for plant-dwelling orders, families, and feeding guilds show the relatedness of sites and species in ordination space with fitted environmental variables (Fig. 2.9, Fig. 2.10, Fig. 2.11)

Discussion

The primary objective of this study was to examine whether prescribed fire and the post fire environment characteristics influenced the arthropod community at mixed-grass mesquite study locations. I did not find a significant influence of prescribed fire on either ground-active or plant-dwelling arthropods

Texas Tech University, Britt Smith, May 2018 at the order or family level for the most abundant taxa. However, I did observe an influence of environmental covariates, which are directly influenced by fire’s consumption of litter and dead vegetation. At the order level, I found a significant influence of percent live vegetation on the assemblage of plant-dwelling arthropods. I specifically found a significant positive influence of percent live vegetation on plant-dwelling Orthoptera. At the family level, I found a significant influence of vegetation visual obstruction on both ground-active and plant- dwelling arthropod assemblages. Further, I also observed a significant influence of percent vegetation on plant-dwelling arthropod families. Also, at the family level, I observed a significant positive influence of percent live vegetation on plant-dwelling Rhyparochromidae. When examining function feeding guilds I found a significant influence of percent live vegetation on my plant-dwelling feeding guilds. Specifically, I observed a negative influence of percent live vegetation on plant-dwelling sap-feeding and positive influence on seed-feeding arthropods. I also observed a significant influence of vegetation visual obstruction on ground-active sap-feeding insects. While there was correlation between my environmental covariates of vegetation visual obstruction and percent bare soil and between vegetation visual obstruction and percent live vegetation, I kept these covariates in my models because they have different potential influence on their respective assemblages.

Though this study did not show a direct influence of fire, sites that received fire treatment had increased bare soil and reduced vegetation visual obstruction. These outcomes are typical of areas that have experienced prescribed

Texas Tech University, Britt Smith, May 2018 fire (Boyd and Bidwell 2001, Fuhlendorf et al. 2006). Percent live vegetation did not differ between burned and unburned sites, which was expected, but is known to influence phytophagous arthropods that feed on living vegetation (Tscharntek and Greiler 1995). I observed a similar response, particularly from plant-dwelling arthropods. I found a negative influence of live vegetation on plant-dwelling sap- feeding arthropods, however, this is likely due to two sites containing high abundance of sap-feeding arthropods and low percentage of live vegetation that were sampled at PR2 in 2014. I observed a positive influence of percent live vegetation on seed-feeding arthropods which is the feeding guild of the

Hemipteran family Rhyparochromidae. Further Orthoptera abundance is known to correlate with live vegetation abundance (Chapman and Joern 1990). I, too, found Orthoptera in greater abundances where percent live vegetation was high.

The response of arthropods to wildland fire is known to vary greatly among orders and families and depends on many environmental variables

(Warren et al. 1987, Swengel 2001). In rangelands of the Great Plains, herbivorous arthropods have been found in greater quantities on burned compared to unburned areas (Nagel 1973, Moran 2014). Herbivores in this study, defined as those consuming solid, living, and attached vegetative matter only, did not differ between burn or unburned sites for ground-active or plant-dwelling strict herbivores. This splitting of plant-feeding arthropods, compared to lumping, could explain differences I found compared to Nagel 1973 and Moranz

2014. Other studies have found spring prescribed fires to negatively impact

Araneae (Engle et al. 2008, Doxon et al. 2011). I did not find a significant post-

Texas Tech University, Britt Smith, May 2018 fire difference in ground-active or plant-dwelling Araneae. This may be due to the duration of time between when prescribed fires were conducted and arthropods were sampled. As arthropods grow and mature, their ability to disperse into new areas increases (Speight et al. 2008). As a result, as time since fire increases, the likelihood of arthropods colonizing recently burned areas is higher (Reed 1997).

In retrospect, a longitudinal study beginning shortly after treatments were established may have better shown differences through post-fire succession.

Overall, this study suggests that prescribed fire can likely be used to reduce woody plant abundance, reduce wildfire potential, and improve forage palatability for livestock without causing irreversible harm to rangeland arthropods in the Texas Rolling Plains. These results suggest that prescribed fire, through indirect impacts to vegetation structure and cover, may be beneficial for several families and feeding guilds. Since previous examinations of arthropod response to prescribed fire show high variability at the community level, focusing on a specific taxon of interest may lead to better conclusions, particularly arthropod species of concern. Further, examination of arthropod life stage, mobility, and feeding guild and wildland fire may also predict arthropod community response to prescribed fire and the post-fire environment (Kral et al.

2017). Wildland fire is a disturbance that has shaped rangelands in this part of

Texas for hundreds of years, and it is likely that many of these native arthropod populations have evolved to cope with such disturbance.

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Literature Cited

Anderson, R. C., Leahy, T., Dillion, S. S., 1989. Numbers and biomass of selected insect groups on burned and unburned sand prairie. American Midland Naturalist 122, 151-162.

Arnett, R. H., 2000. American insects: a handbook of the insects of American north of Mexico. In. 2nd edition. CRC Press, Boca Raton, FL.

Borror, D. J., White, R. E., 1998. A field guide to insects: America north of Mexico. In. 2nd edition. Houghton Mifflin Harcourt, New York, NY.

Chapman, R. F., Joern, A., 1990. Biology of grasshoppers. John Wiley & Sons.

Cully, J. F., 1999. Lone star tick abundance, fire, and bison grazing in tallgrass prairie. Journal of Range Management 52, 139–144.

Curry, J.P., 1994. grassland invertebrates: ecology, influence on soil fertility and effects on plant growth. Springer Science & Business Media.

Doxon, E. D., Davis, C. A., Fuhlendorf, S. D., Winter, S. L., 2011a. Aboveground macroinvertebrate diversity and abundance in sand sagebrush prairie managed with the use of pyric herbivory. Rangeland Ecology & Management 64, 394–403.

Doxon, E.D., Davis, C.A., Fuhlendorf, S.D., 2011b. Comparison of two methods for sampling invertebrates: vacuum and sweep-net sampling. Journal of Field Ornithology 82, 60–67.

Engle, D. M., Fuhlendorf, S. D., Roper, A., Leslie, D. M., 2008. Invertebrate community response to a shifting mosaic of habitat. Rangeland Ecology & Management 61, 55–62.

Hartley, M. K., Rogers, W. E., Siemann, E., Grace, J., 2007. Responses of prairie arthropod communities to fire and fertilizer: balancing plant and arthropod conservation. The American Midland Naturalist 157, 92–105.

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Kral, K. C., Limb, R. F., Harmon, J. P., Hovick, T. J., 2017. Arthropods and fire: previous research shaping future conservation. Rangeland Ecology & Management 70, 589–598.

Koerth, B. H., Mutz, J. L., Segers, J. C., 1986. Availability of bobwhite foods after burning of Pan American Balsamscale. Wildlife Society Bulletin 14, 146– 150.

Moran, M. D., 2014. Bison grazing increases arthropod abundance and diversity in a tallgrass prairie. Environmental Entomology 43, 1174–1184.

Nagel, H. G., 1973. Effect of spring prairie burning on herbivorous and non- herbivorous arthropod populations. Journal of the Kansas Entomological Society 46, 485–496.

NOAA-NCDC (2018). Climate Data Online. https://www.ncdc.noaa.gov/cdo- web/ (accessed 11 January 2018).

Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., 2017. Vegan: community ecology package. R package version 2.4-4.

R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Reed, C., 1997. Responses of prairie insects and other arthropods to prescription burns. Natural Areas Journal 17, 380–385.

Rice, L. A., 1932. The effect of fire on the prairie animal communities. Ecology 13, 392–401.

Robel, R. J., Briggs, J. N., Dayton, A. D., Hulbert, L. C., 1970. Relationships between visual obstruction measurements and weight of grassland vegetation. Journal of Range Management Archives 23, 295–297.

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Samways, M. J., McGeoch, M. A., New, T. R., 2010. Insect conservation: a handbook of approaches and methods. Oxford University Press.

Scasta, J. D., Engle, D. M., Talley, J. L., Weir, J. R., Stansberry, J. C., Fuhlendorf, S. D., Harr, R. N. 2012. Pyric-herbivory to manage horn flies (Diptera: Muscidae) on cattle. Southwestern Entomologist 37:325–334.

Soil Survey Staff, 2018. Web Soil Survey. Available online at the following link: https://websoilsurvey.sc.egov.usda.gov/. (accessed 11 January 2018).

Speight, M.R., Hunter, M.D., Watt, A.D., 2008. Ecology of insects: concepts and applications. Blackwell Science.

Swengel, A. B., 2001. A literature review of insect responses to fire, compared to other conservation managements of open habitat. Biodiversity & Conservation 10, 1141–1169.

Tscharntke, T., Greiler, H. J., 1995. Insect communities, grasses, and grasslands. Annual Review of Entomology 40, 535–558.

Van Amburg, G. L., Swaby, J. A., Pemble, R. H., 1981. Response of arthropods to a spring burn of a tallgrass prairie in northwestern Minnesota. Ohio Biological Survey Biological Notes 15, 240–243.

Wang, Y., Naumann, U., Wright, S. T., Warton, D. I., 2012. mvabund - an R package for model-based analysis of multivariate abundance data: The mvabund R package. Methods in Ecology and Evolution 3:471–474.

Warren, S. D., Scifres, C. J., Teel, P. D., 1987. Response of grassland arthropods to burning: a review. Agriculture, Ecosystems & Environment 19, 105–130.

Warton, D. I., Foster, S. D., De’ath, G., Stoklosa, J., Dunstan, P. K., (2015). Model-based thinking for community ecology. Plant Ecology 216, 669– 682.

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Winter, B. M., 1984. Effects of prescribed burning on avian foraging ecology and arthropod abundance in sagebrush-grassland. M.S. Thesis: Iowa State University

Woodcock, B. A., 2005. Pitfall trapping in ecological studies. in insect sampling in forest ecosystems (S. R. Leather, Editor). Blackwell Science Ltd, pp. 37– 57.

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Tables and Figures

Table 2.1 Relative abundance and counts for all identified subclasses and orders.

Pitfall Traps Sweep Nets Relative Relative Subclass/Order Counts Abundance Subclass/Order Counts Abundance Collembola 183894 0.741553 Hemiptera 16886 0.532278 Hymenoptera 39055 0.157489 Hymenoptera 4747 0.149634 Acari 8770 0.035365 Araneae 4603 0.145095 Symphypleona 3714 0.014977 Coleoptera 2354 0.074202 Araneae 2917 0.011763 Diptera 1543 0.048638 Hemiptera 2820 0.011372 Orthoptera 1142 0.035998 Coleoptera 2809 0.011327 Lepidoptera 149 0.004697 Diptera 2296 0.009259 Phasmatoidea 94 0.002963 Orthoptera 1165 0.004698 Neuropteran 84 0.002648 Archaeognatha 121 0.000488 Lepidoptera larvae 61 0.001923 Isopoda 117 0.000472 Psocodea 19 0.000599 Scorpions 73 0.000294 Mantodean 18 0.000567 Blattodae 70 0.000282 Odonata 15 0.000473 Lepidoptera 57 0.00023 Acari 5 0.000158 Chilopoda 28 0.000113 Symphleona 4 0.000126 Opiliones 15 6.05E-05 Mantodean 13 5.24E-05 Phalangida 11 4.44E-05 Solifugae 10 4.03E-05 Symphypelona 10 4.03E-05 Neuropteran 5 2.02E-05 Neruoptera 4 1.61E-05

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Pseudoscorpiones 3 1.21E-05 Chelonethida 3 1.21E-05 Blattodea 3 1.21E-05

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Table 2.2 Model results from treatment and environmental covariates on ground-active arthropod orders, families, and functional feeding guilds.

Variable Res d.f. d.f. diff dev P-value Order Intercept 21 Burn Treatment 20 1 6.076 0.542 Vegetation Visual Obstruction 19 1 15.617 0.066 Percent Bare Soil 18 1 8.756 0.362

Family Intercept 21 Burn Treatment 20 1 8.99 0.831 Vegetation Visual Obstruction 19 1 36.88 0.022 Percent Bare Soil 18 1 18.95 0.295

Feeding guild Intercept 21 Burn Treatment 20 1 4.937 0.623 Vegetation Visual Obstruction 19 1 15.977 0.054 Percent Bare Soil 18 1 6.949 0.414

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Table 2.3 Model results from treatment and environmental covariates on plant-dwelling arthropod orders, families, and functional feeding guilds.

Variable Res d.f. d.f. diff dev P-value Order Intercept 21 Burn Treatment 20 1 1.86 0.959 Vegetation Visual Obstruction 19 1 9.049 0.307 Percent Live Vegetation 18 1 23.014 0.011 Family Intercept 21 Burn Treatment 20 1 66.73 0.462 Vegetation Visual Obstruction 19 1 140.9 0.015 Percent Live Vegetation 18 1 195.76 0.002 Feeding guild Intercept 21 Burn Treatment 20 1 8.93 0.544 Vegetation Visual Obstruction 19 1 15.52 0.148 Percent Live Vegetation 18 1 29.51 0.009

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Figure 2.1 Study site locations in the Rolling Plains ecoregion of north-central Texas. Map data: Google, DigitalGlobe

Texas Tech University, Britt Smith, May 2018

Figure 2.2 2014 sample sites located at Private Ranch 1. 100 m vegetation and sweep net sample transects colored yellow. 100 m pitfall trap arrays colored cyan.

Texas Tech University, Britt Smith, May 2018

Figure 2.3 2014 and 2015 sampling sites at Private Ranch 2. 2014 sampling sites denoted by yellow vegetation and sweepnet transects. 2015 sampling sites denoted by pink vegetation and sweepnet transects. Map data: Google, DigitalGlobe

Texas Tech University, Britt Smith, May 2018

Figure 2.4 2015 sampling sites located at Matador Wildlife Management Area. Sampling sites are represented by pink vegetation and sweep net transects. Pitfall arrays are excluded for clarity but are located between and parallel to pink transects. Map data: Google, DigitalGlobe

Texas Tech University, Britt Smith, May 2018

Figure 2.5 Accumulation curves for ground-active families (a) and plant- dwelling families (b) with 95% confidence interval.

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Figure 2.6 NMDS ordination of ground-active arthropods showing orders and sites with fitted treatment groups and environmental factors (burned and unburned) and covariates (vegetation visual obstruction [VO] and percent bare soil). Burned (-0.003, -0.003) and unburned (0.014, 0.001) factors share similar coordinates and thus only burned is shown on this ordination. Sites are coded by year with closed circles representing 2014 and closed triangles representing 2015.

Texas Tech University, Britt Smith, May 2018

Figure 2.7 NMDS ordination of ground-active arthropods showing families and sites with fitted treatment groups and environmental factors (burned and unburned) and covariates (vegetation visual obstruction [VO] and percent bare soil). Burned (0.003, -0.005) and unburned (0.002, 0.006) factors share similar coordinates and thus only burned is shown on this ordination. Sites are coded by year with closed circles representing 2014 and closed triangles representing 2015. Families are represented by letters (A = Alydidae, B = Anthicidae, C = Anthocoridae, D = Aphididae, E = Buprestidae, F = Carabidae, G = Cerambycidae, H = Cercopidae, I = Chrysomelidae, J = Cicadellidae, K = Cixiidae, L = Coccinellidae, M = Coccoidae, N = Coreidae, O = Corimelaenidae, P

Texas Tech University, Britt Smith, May 2018

= Corylophidae, Q = Culicidae, R = Curculionidae, S = Cydnidae, T = Delphacidae, U = Elateridae, V = Formicidae, W = Geocoridae, X = Geotrupidae, Y = Histeridae, Z = Issidae, AA = Largidae, BB = Lygaeidae, CC = Meloidae, DD = Melyridae, EE = Miridae, FF = Mutillidae, GG = Mymaridae, HH = Ochodaeidae, II = Pentatomidae, JJ = Phenogodidae, KK = Prioninae, LL = Reduviidae, MM = Rhopalidae, NN = Salpingidae, OO = Scarabaeidae, PP = Scutelleridae, QQ = Silphidae, RR = Staphylinidae, SS = Tenebrionidae, TT = Thyreocoridae, UU = Trogidae, VV = Zopheridae)

Texas Tech University, Britt Smith, May 2018

Figure 2.8 NMDS ordination of ground-active arthropods showing feeding guilds and sites with fitted treatment groups and environmental factors (burned and unburned) and covariates (vegetation visual obstruction [VO] and percent bare soil). Burned (0.007, -0.004) and unburned (0.002, 0.002) factors share similar coordinates and thus only burned is shown on this ordination. Sites are coded by year with closed circles representing 2014 and closed triangles representing 2015.

Texas Tech University, Britt Smith, May 2018

Figure 2.9 NMDS ordination of plant-dwelling arthropods showing orders and sites with fitted treatment groups and environmental factors (burned and unburned) and covariates (vegetation visual obstruction [VO] and percent live vegetation). Burned (-0.001, 0.003) and unburned (-0.004, -0.002) factors share similar coordinates and thus only burned is shown on this ordination. Sites are coded by year with closed circles representing 2014 and closed triangles representing 2015. Hym = Hymenoptera.

Texas Tech University, Britt Smith, May 2018

Figure 2.10 NMDS ordination of plant-dwelling arthropods showing families and sites with fitted treatment groups and environmental factors (burned and unburned) and covariates (vegetation visual obstruction [VO] and percent live vegetation). Burned (0.001, -0.001) and unburned (-0.001, -0.00) factors share similar coordinates and thus only burned is shown on this ordination. Sites are coded by year with closed circles representing 2014 and closed triangles representing 2015. Families are represented by letters (A = Achilidae, B = Acrididae, C = Alydidae, D = Anobiidae, E = Anthicidae, F = Anthocoridae, G = Aphididae, H = Berytidae, I = Blissidae, J = Bostrichidae, L = Buprestidae, M = Cantharidae, N = Carabidae, O = Cerambycidae, P = Cercopidae, Q =

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Chrysomelidae, R = Cicadellidae, S = Cicadidae, T = Cixiidae, U = Cleridae, V = Coccinellidae, W = Coreidae, X = Corimelaenidae, Y = Corylophidae, Z = Curculionidae, AA = Delphacidae, BB = Dictyopharidae, CC = Elateridae, DD = Flatidae, EE = Formicidae, FF = Geocoridae, GG = Gryllidae, HH = Issidae, II = Largidae, JJ = Lygaeidae, KK = Meloidae, LL = Melyridae, MM = Membracidae, NN = Miridae, OO = Mordellidae, PP = Mutillidae, QQ = Mycetophagidae, RR = Nabidae, SS = Nitidulidae, TT = Pentatomidae, UU = Phalacridae, VV = Piesmatidae, WW = Psyllidae, XX = Reduviidae, YY = Rhopalidae, ZZ = Rhyparochromidae, AAA = Scarabaeidae, BBB = Scutelleridae, CCC = Tettigoniidae, DDD = Tingidae)

Texas Tech University, Britt Smith, May 2018

Figure 2.11 NMDS ordination of plant-dwelling arthropods showing feeding guilds and sites with fitted treatment groups and environmental factors (burned and unburned) and covariates (vegetation visual obstruction [VO] and percent live vegetation). Burned (0.007, 0.005) and unburned (-0.004, 0.003) factors share similar coordinates and thus only burned is shown on this ordination. Sites are coded by year with closed circles representing 2014 and closed triangles representing 2015.

Texas Tech University, Britt Smith, May 2018

CHAPTER 3

Northern Bobwhite Chick Prey Responses to Rangeland

Fire.

Abstract

Northern bobwhite quail, an important game species, have been in decline across the United States for several decades. Land management strategies that enhance quail chick brooding areas may help increase chick survival and lead to stable populations. I examined the influence of prescribed rangeland fire on arthropod prey commonly consumed by bobwhite quail chicks. This study took place in mixed grass mesquite rangelands of north central Texas during 2014 and

2015. To sample arthropods, I used pitfall traps to capture ground active arthropods and sweep nets for plant-dwelling arthropods. My model included prescribed fire treatment and environmental covariates of percent bare soil and live vegetation. I did not find an effect of prescribed fire on the arthropod community at the family or order level. I found a significant influence of both bare soil and live vegetation on arthropod family abundances. This study shows that prescribed does not negatively affect quail chick arthropod prey, and in conjunction with the benefits burned areas provide for chick brooding areas, can benefit northern bobwhite quail populations.

Introduction

Northern bobwhite quail (Colinus virginianus; hereafter, bobwhite quail, quail) are an economically important game species whose populations have been 46

Texas Tech University, Britt Smith, May 2018 in range-wide decline for several decades (Sauer et al. 2017). Land use patterns and reduction of habitat are cited as primary drivers of this decline (Brennan

1991, Hernández et al. 2013). Rangelands are an agricultural land use that provide beneficial bobwhite quail habitat to sustain local populations; land management activities within rangelands can positively or negatively affect quail populations. For example, heavy cattle stocking rates on low productivity rangeland can reduce important herbaceous cover for nesting sites (Townsend et al. 2001). At the opposite extreme, lack of grazing in productive rangelands, or lack of management in general, can lead to thick vegetation unsuitable for brooding chicks (Taylor et al. 1999). Rangeland management techniques (i.e., disking) that increase forb seed production, and thus make more food available to adult bobwhite quail can positively impact their populations. Prescribed fire is a rangeland disturbance and management tool that reduces woody plant abundance and increases cattle forage productivity (Engle and Bidwell 2001).

Prescribed fire benefits bobwhite quail in rangelands by reducing plant biomass and consuming dead vegetation creating open brooding habitat (Greenfield et al.

2003, Yeiser et al. 2015); thus, brooding areas that maintain or increase survivorship of chicks should help sustain quail populations.

Northern bobwhite quail chicks require diets containing a minimum of

20% protein, and optimally 28% protein (Nestler et al. 1942, Baldini et al 1950,

Andrews et al. 1973). In chicks that were fed a low protein diets (<15% protein), significant reductions in growth rate occurred, and those fed less than 8% protein diets experienced compromised immune systems (Lochmiller et al. 1993). This dietary protein comes almost entirely from the consumption of arthropods.

Texas Tech University, Britt Smith, May 2018

Other gallinaceous species' chicks also require arthropod protein for growth and development (Dale and DeWitt 1958, Potts and Aebischer 1991, Park et al. 2001).

A nutritional analysis of human-consumed insects in Nigeria found crude protein ranges from 6.2% to 30% with a mean of 21.4% for 15 species in the orders

Orthoptera, Lepidoptera, Coleoptera, Hymenoptera, and Isoptera (Banjo et al.

2006).

The first 30 days after hatching are a sensitive period for quail chicks

(Lusk et al. 2005). In western Oklahoma, only 38% of chicks survived from 0-20 days, and 97% of chicks survived days 21-39; however, daily survival rates for the two age periods were not significantly different (0.95 and 0.99 respectively;

DeMaso et al. 1997). Other studies have shown a range of survival estimates for bobwhite chicks in the first two weeks post hatching ranging from 0.13 to 0.29

(Cantu and Everett 1982, DeVos and Mueller 1993). During this post-hatching period, several habitat requirements are important for bobwhite chick survival.

In particular, northern bobwhite quail preferentially select areas with greater amounts of bare ground, forb cover, and succulent vegetation as brooding grounds (Taylor and Guthery 1994, Taylor et al. 1999). These brooding ground selections may likely relate to arthropod abundance and ease of capture (Doxon and Carroll 2010). In contrast, tall vegetation, shrubs, and high litter are preferred characteristics of bobwhite nesting locations, which is due to nest concealment (Taylor et al. 1999, Townsend et al. 2001, Lusk et al. 2006).

Prescribed fire has the potential to influence the survival rate of bobwhite chicks through alteration of vegetation structure and by influencing the arthropod prey availability. Adult northern bobwhite quail densities are inversely

Texas Tech University, Britt Smith, May 2018 related to vegetation visual obstruction and positively related to increasing grass heterogeneity (Ransom and Schulz 2007). In northern Louisiana, bobwhite chicks captured more arthropods in pine forest treated with fire and imazapyr herbicide compared to fire alone, no treatment, and mowing (Burke et al. 2008).

In northern Mississippi, burned grassland right-of-ways were found to contain higher densities of arthropods and that bobwhite chicks consumed insects common on the burned areas (Hurst 1970). Many studies have separately examined the influence of prescribed fire on arthropods and arthropod selection of bobwhite chicks, but only a few studies, all in the southeast United States, have examined prescribed fire's influence on the availability of arthropod prey of bobwhite chicks (e.g., Hurst 1970, Kitts 2004, Burke et al. 2008).

My objective was to evaluate the influence of prescribed rangeland fire on

Prescribed fire enhances quail brooding areas by reducing litter and dead vegetation; thus, I hypothesized that prescribed fire will have a positive impact on commonly consumed arthropods. I included the covariates of percent bare soil and percent live vegetation cover because these variables can influence the abundance of arthropods accessible to bobwhite quail chicks.

Methods

Study Area

I conducted my study on 2 private ranches and a public wildlife management area. Private ranch 1 (PR1) was sampled in 2014 only and is located in Archer County, Texas (33.57 N, 98.71 W; Fig. 2.2). Private ranch 2 (PR2) was sampled in both 2014 and 2015 and is located in Dickens County, Texas (33.42 N,

Texas Tech University, Britt Smith, May 2018

100.88 W; Fig. 2.3). Matador Wildlife Management Area (WMA), which is owned and operated by the Texas Parks and Wildlife Department, was sampled in 2015 only and is located in Cottle County, Texas (34.11 N, 100.35; Fig. 2.4).

2018). Precipitation in 2014 at PR1 was 487 mm. Soils located in each sample area range from fine sandy loam to clay loam (Soil Survey Staff 2018). Pastures at

PR1 are typically grazed with cattle, but in my study, cattle were deferred from study areas. The elevation at PR1 is 307 m.

In 2014, I opportunistically sampled a 12 ha wildfire that took place between 4-11 May 2014 at PR2. In 2015 I conducted 3 prescribed fires on 10

April, which were 24, 33, and 50 ha in size. Thirty year mean annual precipitation and temperature are 577 mm and 16.2˚C, respectively (NOAA-NCDC 2018).

Annual precipitation in 2014 and 2015 were 517 mm and 850 mm. My sampling areas were all dominantly clay loam soils (Soil Survey Staff 2018). The elevation at PR2 is 703 m.

In 2015, Texas Parks and Wildlife conduced 3 prescribed fires at Matador

Survey Staff 2018). The elevation at Matador WMA is 526 m.

Texas Tech University, Britt Smith, May 2018

Environmental Variables

To measure vegetation covariates, I established two 100 m transects parallel and 25 m to either side of my pitfall trap array at each sampling site. To sample vegetation cover placed a 1 m2 quadrate frame at each 10 m mark along the transect and estimated vegetation cover to the nearest 5% for bare soil, live vegetation, dead vegetation, and litter. To estimate vegetation structure, I used a modified Robel pole method, in which vegetation visual obstruction was measured to the nearest 1 cm on a Robel pole at a distance of 2 m from the pole and 1 m above the ground (Robel et al. 1970). Four measurements were taken for each cardinal direction every 10 m along my 100 m transects. I averaged the measurements along the two transects to give mean vegetation visual obstruction, percent bare soil, and percent live vegetation for each sampling site.

I took vegetation samples between 4 – 5 August 2014 and between 29 June and

29 July 2015.

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Arthropod Sampling

To sample for ground-dwelling arthropods, I used pitfall trap arrays consisting of 5 pitfall traps placed 20 m apart along a 100 m transect. Pitfall traps consisted of two 500 mL plastic cups filled with 100 mL water, 50 mL propylene glycol, and a drop of unscented dish detergent (Samways et al. 2010). Each pitfall trap was covered with a polystyrene plate anchored with sod staples to prevent rainfall from entering the trap (Woodcock 2005). I collected and stored trap contents in 532 mL Whirl-pak™ bags (Nasco, Fort Atkinson, WI). I collected and refilled pitfall traps every 3-5 days for six consecutive windows during the sampling period in both years.

To sample plant-dwelling arthropods, I swept two 100 m transects 25 m away from and parallel to my pitfall trap array. I conducted sweeps by passing a

30.5 cm diameter sweep net through vegetation just above the soil surface in a

180-degree arc while walking along the transect. Every 50 m, I emptied the contents of the sweep net into a 3.78 L resealable plastic bag, in which arthropods were euthanized with a cotton ball containing ethyl acetate. Samples were then stored in an Idylis model ICM070LC freezer at -5 ˚C. I identified adult arthropods to family level except for Araneae, which was identified at the order level, using readily available taxonomic keys (Borror and White 1998, Arnett

2000).

Bobwhite Quail Chick Selected Taxa

To determine the arthropods that are commonly consumed by bobwhite quail chicks, I collected published studies examining arthropod consumption by gallinaceous chicks. I selected 10 publications related to bobwhite quail chicks

Texas Tech University, Britt Smith, May 2018 which included 4 peer-reviewed journal articles, 3 conference proceedings, 2

Ph.D. dissertations, and one M.S. thesis (Table 3.1). From these publications, I assigned a selection affinity ranking to arthropods based on relative consumption by bobwhite quail chicks. This ranking was based on comparative consumptions of arthropods by quail chicks per examined study. If a particular taxon were selected more than other taxa it was considered to have a high selection affinity ranking. If the rate of consumption of a taxon was intermediate to other taxa then the taxon was assigned a rank of medium. If consumption of a taxon was least compared to others it was ranked as low selection affinity. From this ranking, I selected five taxa for analysis that ranked generally between high and medium selection affinity. From my list, the chosen taxa for analysis included orders of

Coleoptera, Hemiptera, Isopoda, Araneae, and the family Formicidae.

Analysis

All analyses were conducted using the program R (R core team 2017). I conducted my analysis of bobwhite quail chick prey at both the family and order levels. Pitfall traps and sweep net sampling were examined separately due to differences in each sampling technique’s accumulation of arthropods. Pitfall traps containing dead vertebrates were removed from analysis due to high abundance of taxa attracted to carrion (N = 25). As a result, I calculated mean pitfall trap contents at each treatment site. Rare families (i.e., those containing only one individual across the study) were removed from both pitfall trap and sweep net analysis. To examine prey responses to prescribed fire, I used model-based multivariate methods (Warton et al. 2015). Model-based multivariate methods enable us to account for the mean-variance relationship of multivariate data and

Texas Tech University, Britt Smith, May 2018 examine model validation in relation to observed data (Warton et al. 2015). To examine the arthropod prey of quail chicks, I analyzed taxa counts using a generalized linear model with a negative binomial distribution. I further examined individual taxon responses to environmental factors and covariates using post hoc univariate tests adjusted for multiple comparisons with step-down resampling. I used an additive model with burn treatment factors, percent bare soil, and percent live vegetation as covariates. I conducted analysis with the function anova.manyglm in the package mvabund version 3.12.3 in R (Wang et al. 2012). I validated model assumptions by examining the mean-variance relationship for a quadratic correlation and log-linearity assumption for unusual patterns of residuals compared to fitted values. To visualize my analysis, I created an ordination plot using nonmetric multidimensional scaling (NMDS) with Bray-

Curtis distances and run with 2 dimensions. To reduce the influence of extreme count values, I applied a square root transformation with a Wisconsin double standardization. My ordination was created using the vegan package version 2.4-

4 in R (Oksanen et al. 2017).

Results

From my pitfall traps, I identified 45,844 individuals from 38 families

(excluding Araneae) from my 5 target orders. The most abundant family was

Formicidae with 37,601 individuals accounting for 82% of trapped arthropods, followed by the order Araneae and family Cicadellidae with 2,917 and 1,071 respective individuals. Percent bare soil was significantly greater in burned treatments (35.3%) compared to unburned (11.8%, Welch’s t test, t = 5.82, df =

Texas Tech University, Britt Smith, May 2018

18.17, P < 0.001). Percent live vegetation was not significantly different between treatments (Welch’s t test, t = -0.0069, df = 19.95, P = 0.99). Model results did not show a significant influence of prescribed fire on my selected taxa (dev =

29.91, P = 0.685). However, my covariates, percent bare soil (dev = 72.64, P =

0.011) and percent live vegetation (dev = 89.55, P = 0.003), significantly influenced my overall taxa. Results of my univariate analysis found no significance influence of treatment or covariates on individual taxa. Analysis at the order level showed no significant results for treatment (dev = 2.78, P =

0.761), percent bare soil (dev = 7.28, P = 0.260), or percent live vegetation (dev =

14.14, P = 0.064). I also found no significant influence of my environmental factors or covariates on abundances within orders.

Sweep nets sampling captured 25,398 individuals in 49 families (excluding

Araneae) from 4 of my 5 target orders. Cicadellidae was the most abundant family with 9,443 individuals netted. The second and third most abundant taxa were Araneae (N = 4,603) and Delphacidae (N = 1866). Model results did not show a significant influence of prescribed fire treatment (dev = 60.06, P = 0.497).

However, I also saw a significant influence of percent bare soil (dev = 123.15, P =

0.016) and percent live vegetation (dev = 218.18, P = 0.004). Results from the univariate analyses showed a significant negative influence of percent bare soil on the family Coccinellidae (N = 479, dev = 12.86, P = 0.047). I also a significant positive influence of percent live vegetation on the families Rhyparochromidae (N

= 390, dev = 22.13, P = 0.020) and Scutelleridae (N = 201, dev = 17.56, P =

0.046). Analysis at the order level found no significant results for treatment (dev

= 1.03, P = 0.948), percent bare soil (dev = 11.13, P = 0.114), or percent live

Texas Tech University, Britt Smith, May 2018 vegetation (dev = 13.05, P = 0.057). I also found no significant influence of my environmental factors or covariates on individual orders.

NMDS results show the influence of my covariates on the selected arthropod families. Along axis NMDS1 a separation of sites occurs by sampling year for both pitfall traps and sweep nets. The stress of these NMDS ordinations for pitfall traps and sweep nets were 0.17 and 0.16, respectively (Fig. 3.1, Fig. 3.2).

Discussion

I found no influence of prescribed fire treatment on ground and plant- dwelling arthropods commonly consumed by bobwhite quail chicks. However, I observed an influence of percent bare soil and percent live vegetation on ground and plant-dwelling assemblages. Though no individual ground-dwelling arthropod family showed a response to covariates, I did see a negative influence of bare soil on the family Coccinellidae and positive influence of live vegetation on families Rhyparochromidae and Scutelleridae. From the NMDS ordination, a separation of sites by year is apparent and likely has an influence on collected arthropods. Regarding my hypothesis, I did not see a direct influence of prescribed fire on arthropod availability for northern bobwhite quail chicks.

While I did not observe direct fire effects, both my covariates had a significant influence on ground and plant-dwelling arthropod composition at the family level. Percent bare soil was greater in recently burned areas, which is likely due to consumption of litter during the prescribed burn. Litter is an important food source for detritivorous arthropods and those arthropods which require cool, moist refuge (e.g., Armadillidiidae). The soil surface in burned areas with

Texas Tech University, Britt Smith, May 2018 higher amounts of bare soil have temperatures greater during the day and cooler at night compared to litter insulated soils in unburned areas (Brown 1967).

Percent bare soil had a negative influence on Coccinellidae. Coccinellidae are generally plant-dwelling insects at all life stages and prey on plant-dwelling phytophagous insects; however, high areas of bare soil may indicate low total plant biomass in the area. Coccinellidae collected through sweep nets were adult winged males and could have been affected by increased wind speeds due to reduced vegetation and increased bare soil.

Percent live vegetation also significantly influenced arthropod families and had a significant positive influence on Rhyparochromidae and Scutelleridae. At my study sties, 2015 had higher than average rainfall, while 2014 was lower than average. This difference in precipitation is likely the cause of the increased percentage of live vegetation within my sample plots. The influence of precipitation on vegetation may influence arthropod communities since increased plant food sources should lead to increased arthropod abundances

(Benson et al. 2007). This may explain the correlation between percent live vegetation and Rhyparochromidae and Scutelleridae abundance.

Rhyparochromidae are seed-feeding true bugs and increased precipitation could lead to increased seed production. Scutelleridae are sap-feeding true bugs, thus an increase in live vegetation should increase their abundance. However, I did not see similar responses with other hemipterans in my study that share similar feeding and life history strategies. Increasing the number of replicates may lead to detecting a response of other sap-feeding hemipterans to percent live vegetation.

Texas Tech University, Britt Smith, May 2018

Arthropods are a large taxonomic group, and as a result, taxonomic resolution is an important consideration when examining the diet of bobwhite quail chicks. The quail chick diet studies I examined generally identified consumed taxa to at least order, a few to family, and only one beyond family

(Table 3.1). Some have suggested that family or genus level is a reasonable taxonomic resolution for studies on terrestrial arthropod biodiversity (Timms et al. 2013, Driessen and Kirkpatrick 2017). My study focuses on the family level because taxa at higher resolution often have similar feeding and life history strategies; therefore, they should have similar responses to the measured changes in habitat. At the order level, many families within, particularly Coleoptera and

Hemiptera, have vastly different feeding and life history strategies (ie. Carabidae and Chrysomelidae). A caveat of my study is that Araneae were not identified to family. However, most of the Araneae collected in pitfall traps were surface active ground spiders and those caught in sweep nets included the families Oxyopidae,

Araneidae, Salticidae, and Thomisidae. Since many of the bobwhite quail chick diet studies examined prey at the order level, it is difficult to pin down a particular ecology of prey taxa. I recommend that future studies examine bobwhite quail chick diet and identify prey taxa down to at least the family level.

Of the family level taxa my quail chick diet studies examined, Carabidae,

Chrysomelidae, Cicadellidae, Nabidae, Formicidae, and Armadillididae have reasonably high selection affinity and had high abundances in my study.

However, I did not see an influence of my prescribed fire treatments or my environmental covariates on these taxa.

Texas Tech University, Britt Smith, May 2018

Future bobwhite quail chick diet studies should examine the color of selected arthropod taxa. Many arthropods, particularly insects, have aposematic coloration or Batesian mimicry to avoid and hinder predation. Quail chicks are able to see color and may have innate or learned avoidance of such prey species.

Studies have examined bobwhite quail adult and chick response to color, and suggest avoidance of insect colored orange or red (Mastrota and Mench 1995,

Gionfriddo and Best 1996). Incorporating this information into studies examining composition of quail chick selected taxa could improve inference of management decisions on prey taxa. Prey size of bobwhite quail chicks has been estimated to range from roughly 0.5 – 1.5 mm (Smith and Burger 2005). Another study suggested 2 – 5 mm as an optimal size for quail chicks (Foye et al. 2015).

This size range is similar to other gallinaceous bird chicks, specifically grey partridge and ringed-necked pheasant (Moreby et al. 2006, Whitmore et al.

1986). The size estimations could be used to further reduce the number of taxa examined in future studies by removing large insects in taxa that are unlikely to be consumed by bobwhite quail chicks.

Management Implications

Prescribed fire is a land management tool known to create beneficial, patchy habitat for bobwhite quail chick brooding areas. The results of my study do not suggest a negative influence of prescribed fire on arthropods consumed by quail chicks. In my study I saw a significant influence of bare soil, which increases on recently burned areas, on quail chick selected taxa. Increased bare soil on recently burned areas may increase movement of ground active

Texas Tech University, Britt Smith, May 2018 arthropods (e.g., black body effect, migration, colonization; Caillon et al. 2014) and allow quail chicks easier access to these arthropods compared to unburned areas containing a litter layer. I also saw a significant influence of live vegetation on quail chick selected taxa, though I believe this to be more due to variation in annual precipitation. Prescribed fire generates beneficial habitat conditions that may help curtail northern bobwhite quail decline by enhancing quail chick brooding areas.

Literature Cited

Andrews, T.L., Harms, R.H., Wilson, H.R., 1973. Protein requirement of the bobwhite chick. Poultry Science 52, 2199–2201.

Arnett, R.H., 2000. American Insects: A handbook of the insects of American north of Mexico, 2nd ed. CRC Press, Boca Raton, FL.

Baldini, J.T., Roberts, R.E., Kirkpatrick, C.M., 1950. A study of the protein requirements of bobwhite quail reared in confinement in battery brooders to eight weeks of age. Poultry Science 29, 161–166.

Banjo, A.D., Lawal, O.A., Songonuga, E.A., 2006. The nutritional value of fourteen species of edible insects in southwestern Nigeria. African Journal of Biotechnology 5, 298–301.

Benson, T.J., Dinsmore, J.J., Hohman, W.L., 2007. Responses of plants and arthropods to burning and disking of riparian habitats. Journal of Wildlife Management 71, 1949–1957.

Borror, D.J., White, R.E., 1998. A field guide to insects: America north of Mexico, 2nd ed. Houghton Mifflin Harcourt, New York, NY.

Brennan, L.A., 1991. How can I reverse the northern bobwhite population decline? Wildlife Society Bulletin 19, 544–555.

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Brown, C.T., 1967. Growth and energy relationships on burned and unburned prairie in southern Wisconsin. M.S. Thesis: University of Wisconsin, Madison, WI.

Burke, J.D., Chamberlain, M.J., Geaghan, J.P., 2008. Effects of understory vegetation management on brood habitat for northern bobwhites. The Journal of Wildlife Management 72, 1361–1368.

Caillon, R., Suppo, C., Casas, J., Arthur Woods, H., Pincebourde, S., 2014. Warming decreases thermal heterogeneity of leaf surfaces: implications for behavioural thermoregulation by arthropods. Functional Ecology 28, 1449–1458.

Cantu, R., Everett, D., 1982. Reproductive success and brood survival of bobwhite quail as affected by grazing practices, in: Proceedings of the National Quail Symposium. pp. 79–83.

Dale, F.H., DeWitt, J.B., 1958. Calcium, phosphorus and protein levels as factors in the distribution of the pheasant. Transactions of the North American Wildlife Conference 23, 5.

DeMaso, S.J., Peoples, A.D., Cox, S.A., Parry, E.S., 1997. Survival of northern bobwhite chicks in western Oklahoma. The Journal of Wildlife Management 61, 846–853.

DeVos, T., Mueller, B., 1993. Reproductive ecology of northern bobwhite in north Florida, in: Proceedings of the National Quail Symposium. pp. 83–90.

Doxon, E.D., Carroll, J.P., 2010. Feeding ecology of ring-necked pheasant and northern bobwhite chicks in conservation reserve program fields. The Journal of Wildlife Management 74, 249–256.

Driessen, M.M., Kirkpatrick, J.B., 2017. Higher taxa can be effective surrogates for species-level data in detecting changes in invertebrate assemblage structure due to disturbance: a case study using a broad range of orders. Austral Entomology.

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Engle, D.M., Bidwell, T.G., 2001. Viewpoint: The response of central north american prairies to seasonal fire. Journal of Range Management 54, 2– 10.

Foye, S., Greenwood, C.M., Masloski, K., Payton, M., 2015. Ground-dwelling arthropod communities related to nesting success of northern bobwhite at two western Oklahoma wildlife management areas. Southwestern Entomologist 40, 463–478.

Gionfriddo, J.P., Best, L.B., 1996. Grit color selection by house sparrows and northern bobwhites. The Journal of Wildlife Management 60, 836–842.

Greenfield, K.C., Chamberlain, M.J., Burger, L.W., Kurzejeski, E.W., 2003. Effects of burning and discing conservation reserve program fields to improve habitat quality for northern bobwhite (Colinus virginianus). The American Midland Naturalist 149, 344–353.

Hernández, F., Brennan, L.A., DeMaso, S.J., Sands, J.P., Wester, D.B., 2013. On reversing the northern bobwhite population decline: 20 years later. Wildlife Society Bulletin 37, 177–188.

Hurst, G.A., 1970. The effects of controlled burning on arthropod density and biomass in relation to bobwhite quail (Colinus virginianus) brood habitat. Ph.D. Dissertation: Mississippi State University, Starkville, MS.

Kitts, C.L., 2004. Individual and landscape-level effects of selective herbicides, mowing, and prescribed fire on habitat quality for northern bobwhite (Colinus virginianus) M.S. Thesis: Louisiana State University, Baton Rouge.

Lochmiller, R.L., Vestey, M.R., Boren, J.C., 1993. Relationship between protein nutritional status and immunocompetence in northern bobwhite chicks. The Auk 110, 503–510.

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Lusk, J.J., Guthery, F.S., Cox, S.A., Demaso, S.J., Peoples, A.D., 2005. Survival and growth of northern bobwhite chicks in western Oklahoma. American Midland Naturalist 153, 389–395.

Lusk, J.J., Smith, S.G., Fuhlendorf, S.D., Guthery, F.S., 2006. Factors influencing northern bobwhite nest-site selection and fate. The Journal of Wildlife Management 70, 564–571.

Mastrota, F., Mench, J.A., 1994. Avoidance of dyed food by the northern bobwhite. Applied Animal Behaviour Science 42, 109–119.

Moreby, S.J., Aebischer, N.J., Southway, S., 2006. Food preferences of grey partridge chicks, Perdix perdix, in relation to size, colour and movement of insect prey. Animal Behaviour 71, 871–878.

Nestler, R.B., Bailey, W.W., McClure, H.E., 1942. Protein requirements of bobwhite quail chicks for survival, growth, and efficiency of feed utilization. The Journal of Wildlife Management 6, 185–193.

NOAA-NCDC, 2018. Climate Data Online. https://www.ncdc.noaa.gov/cdo-web/ (accessed 11 January 2018).

Park, K.J., Robertson, P.A., Campbell, S.T., Foster, R., Russell, Z.M., Newborn, D., Hudson, P.J., 2001. The role of invertebrates in the diet, growth and survival of red grouse (Lagopus lagopus scoticus) chicks. Journal of Zoology 254, 137–145.

Potts, G., Aebischer, N.J., 1991. Modelling the population dynamics of the grey partridge: conservation and management. Bird population studies: their relevance to conservation and management 373–390.

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R Core Team, 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Ransom, D., Schulz, G.G., 2007. Northern bobwhites and postfire succession. The Journal of Wildlife Management 71, 565–570.

Samways, M.J., McGeoch, M.A., New, T.R., 2010. Insect conservation: a handbook of approaches and methods. Oxford University Press.

Sauer, J.R., Link, W.A., Fallon, J.E., Pardieck, K.L., Ziolkowski, D.J., 2013. The North American breeding bird survey 1966–2011: summary analysis and species accounts. North American Fauna 79, 1–32.

Smith, M.D., Burger, L.W., Jr., 2005. Use of imprinted northern bobwhite chicks to assess habitat-specific arthropod availability. Wildlife Society Bulletin 33, 596–605.

Soil Survey Staff. 2018. Web Soil Survey. Available online at the following link: https://websoilsurvey.sc.egov.usda.gov/. (accessed 11 January 2018).

Taylor, J.S., Guthery, F.S., 1994. Components of northern bobwhite brood habitat in southern Texas. The Southwestern Naturalist 39, 73–77.

Taylor, J.S., Church, K.E., Rusch, D.H., 1999. Microhabitat selection by nesting and brood-rearing northern bobwhite in Kansas. The Journal of Wildlife Management 63, 686–694.

Timms, L.L., Bowden, J.J., Summerville, K.S., Buddle, C.M., 2013. Does species- level resolution matter? Taxonomic sufficiency in terrestrial arthropod biodiversity studies. Insect Conservation and Diversity 6, 453–462

Townsend, D.E., II, Masters, R.E., Lochmiller, R.L., Leslie, D.M., Jr., Demaso, S.J., Peoples, A.D., 2001. characteristics of nest sites of northern

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bobwhites in western Oklahoma. Journal of Range Management 54, 260– 264.

Wang, Y., Naumann, U., Wright, S.T., Warton, D.I., 2012. mvabund - an R package for model-based analysis of multivariate abundance data: The mvabund R package. Methods in Ecology and Evolution 3, 471–474.

Warton, D.I., Foster, S.D., De’ath, G., Stoklosa, J., Dunstan, P.K., 2015. Model- based thinking for community ecology. Plant Ecology 216, 669–682.

Whitmore, R.W., Pruess, K.P., Gold, R.E., 1986. Insect food selection by 2-week- old ring-necked pheasant chicks. The Journal of Wildlife Management 50, 223–228.

Woodcock, B.A., 2005. Pitfall Trapping in Ecological Studies, in: Leather, S.R. (Ed.), Insect Sampling in Forest Ecosystems. Blackwell Science Ltd, pp. 37–57.

Yeiser, J.M., Baxley, D.L., Robinson, B.A., Morgan, J.J., 2015. Using prescribed fire and herbicide to manage rank native warm season grass for northern bobwhite. The Journal of Wildlife Management. 79, 69–76.

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Tables and Figures

Table 3.1. Arthropods positively selected by northern bobwhite quail chicks. Selection affinity determined based on selection relative to other taxa described by author.

Order Higher Taxonomic Selection Method State Literature Cited Resolution Affinity Araneae Low Fecal GA Butler et al. 2012 Araneae Low Crop MS Smith and Burger 2005 Araneae Low Crop MS Jackson et al. 1987 Araneae Medium Crop and Gizzard MS Hurst 1972 Araneae Medium Crop and Gizzard NC Moorman et al. 2013 Araneae Low Crop LA Kitts 2004 Araneae Low Crop and Gizzard KS Doxon and Carroll 2010 Araneae Gnaphosidae Medium Crop IL Osborn et al. 2012 Araneae Oxyopidae Medium Crop IL Osborn et al. 2012 Araneae Lycosidae Medium Crop and Gizzard MS Hurst 1970 Opiliones Low Crop IL Osborn 2010 Chilopoda Low Crop and Gizzard NC Moorman et al. 2013 Coleoptera High Fecal GA Butler et al. 2012 Coleoptera Low Crop MS Smith and Burger 2005 Coleoptera High Crop and Gizzard MS Hurst 1972 Coleoptera High Crop MS Jackson et al. 1987 Coleoptera High Crop and Gizzard NC Moorman et al. 2013 Coleoptera Low Crop LA Kitts 2004 Coleoptera Carabdae High Crop MS Jackson et al. 1987 Coleoptera Carabidae High Crop and Gizzard KS Doxon and Carroll 2010

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Coleoptera Carabidae Low Crop and Gizzard NC Moorman et al. 2013 Coleoptera Carabidae Low Crop IL Osborn 2010 Coleoptera Chrysomelidae High Crop MS Jackson et al. 1987 Coleoptera Chrysomelidae High Crop and Gizzard KS Doxon and Carroll 2010 Coleoptera Chrysomelidae Medium Crop IL Osborn et al. 2012 Coleoptera Chrysomelidae Systema elongata High Crop and Gizzard MS Hurst 1970 Coleoptera Tenebrionidae Medium Crop and Gizzard NC Moorman et al. 2013 Coleoptera Tenebrionidae Low Crop IL Osborn 2010 Coleoptera Curculionidae Low Crop and Gizzard NC Moorman et al. 2013 Coleoptera Curculionidae High Crop and Gizzard KS Doxon and Carroll 2010 Coleoptera Curculionidae High Crop and Gizzard MS Hurst 1970 Dermaptera Low Crop and Gizzard NC Moorman et al. 2013 Diptera Low Crop MS Jackson et al. 1987 Diptera Low Crop and Gizzard MS Hurst 1972 Diptera Low Crop and Gizzard NC Moorman et al. 2013 Diptera Low Crop LA Kitts 2004 Diptera Low Crop IL Osborn et al. 2012 Hemiptera High Fecal GA Butler et al. 2012 Hemiptera High Crop MS Smith and Burger 2005 Hemiptera High Crop and Gizzard MS Hurst 1972 Hemiptera High Crop MS Jackson et al. 1987 Hemiptera Medium Crop LA Kitts 2004 Hemiptera High Crop and Gizzard NC Moorman et al. 2013 Hemiptera Pentatomidae Low Crop and Gizzard NC Moorman et al. 2013 Hemiptera Pentatomidae Medium Crop and Gizzard MS Hust 1970 Hemiptera Nabidae Low Crop and Gizzard NC Moorman et al. 2013 Hemiptera Nabidae High Crop and Gizzard KS Doxon and Carroll 2010 Hemiptera Cicadellidae Low Crop and Gizzard NC Moorman et al. 2013 Hemiptera Cicadellidae Medium Crop and Gizzard MS Hurst 1970

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Hemiptera Cicadellidae Medium Crop IL Osborn et al. 2012 Hemiptera Cydnidae Corimelaena marginella Medium Crop and Gizzard MS Hurst 1970 Hemiptera Lygaeidae Oedancala crassimana Medium Crop and Gizzard MS Hurst 1970 Hemiptera Geocoridae Geocoris sp. Medium Crop and Gizzard MS Hurst 1970 Hemiptera Auchenorrhyncha Low Crop MS Jackson et al. 1987 Hemiptera Auchenorrhyncha Low Crop and Gizzard KS Doxon and Carroll 2010 Hymenoptera Medium Fecal GA Butler et al. 2012 Hymenoptera Medium Crop MS Smith and Burger 2005 Hymenoptera Low Crop and Gizzard MS Hurst 1972 Hymenoptera High Crop LA Kitts 2004 Hymenoptera Formicidae High Crop IL Osborn et al. 2012 Hymenoptera Formicidae High Crop and Gizzard KS Doxon and Carroll 2010 Hymenoptera Formicidae High Crop and Gizzard NC Moorman et al. 2013 Isopoda Medium Crop MS Smith and Burger 2005 Isopoda Armadillidiidae High Crop IL Osborn et al. 2012 Lepidoptera (larvae) Medium Crop MS Jackson et al. 1987 Lepidoptera (larvae) Low Crop and Gizzard NC Moorman et al. 2013 Lepidoptera (larvae) Low Crop and Gizzard KS Doxon and Carroll 2010 Lepidoptera (larvae) Low Crop IL Osborn et al. 2012 Orthoptera Low Fecal GA Butler et al. 2012 Orthoptera Medium Crop and Gizzard MS Hurst 1972 Orthoptera Low Crop LA Kitts 2004 Orthoptera Low Crop and Gizzard KS Doxon and Carroll 2010 Orthoptera Low Crop IL Osborn et al. 2012 Orthoptera Acrididae Low Crop and Gizzard NC Moorman et al. 2013 Orthopera Melanoplus spp. Low Crop and Gizzard MS Hurst 1970 Orthoptera Tettigoniidae Conocephalus strictus Low Crop and Gizzard MS Hurst 1970

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Figure 3.1 NMDS ordination of ground-active arthropods caught in pitfall traps showing families and sites with fitted treatment groups and environmental factors and covariates. Families are color coded by order. Species are represented by letters: A = Alydidae, B = Anthicidae, C = Aphididae, D= Araneae, E = Armadillidiidae, F = Carabidae, G = Cerambycidae, H = Cercopidae, I = Chrysomelidae, J = Cicadellidae, K = Cixiidae, L = Coccoidae, M = Coreidae, N = Corimelaenidae, O = Corylophidae, P = Curculionidae, Q = Cydnidae, R = Delphacidae, S = Elateridae, T = Formicidae, U = Geocoridae, V = Geotrupidae, W = Histeridae, X = Issidae, Y = Largidae, Z = Lygaeidae, AA = Meloidae, BB = Miridae, CC = Ochodaeidae,

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DD = Pentatomidae, EE = Prioninae, FF = Reduviidae, GG = Rhopalidae, HH = Scarabaeidae, II = Silphidae, JJ = Staphylinidae, KK = Tenebrionidae, LL = Trogidae, MM = Zopheridae

Texas Tech University, Britt Smith, May 2018

Figure 3.2 NMDS ordination of plant-dwelling arthropods captured in sweep nets showing families and sites with fitted treatment groups and environmental factors and covariates. Families are color coded by order. Species are represented by letters: A = Achilidae, B = Alydidae, C = Anobiidae, D = Anthicidae, E = Anthocoridae, F = Aphididae, G = Araneae, H = Berytidae, I = Blissidae, J = Bostrichidae, K = Burprestidae, L = Cantharidae, M = Carabidae, N = Cerambycidae, O = Cercopidae, P = Chrysomelidae, Q = Cicadellidae, R = Cicadidae, S = Cixiidae, T = Cleridae, U = Coccinellidae, V =

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Coreidae, W = Corimelaenidae, X = Corylophidae, Y = Curculionidae, Z = Delphacidae, AA = Dictyopharidae, BB = Elateridae, CC = Flatidae, DD = Formicidae, EE = Geocoridae, FF = Issidae, GG = Largidae, HH = Lygaeidae, II = Meloidae, JJ = Melyridae, KK = Membracidae, LL = Miridae, MM = Mordellidae, NN = Nabidae, OO = Nitidulidae, PP = Pentatomidae, QQ = Phalacridae, RR = Psyllidae, SS = Reduviidae, TT = Rhopalidae, UU = Rhyparochromidae, VV = Scarabaeidae, WW = Scutelleridae, XX = Tingidae

Texas Tech University, Britt Smith, May 2018

CHAPTER 4

Plant-dwelling Orthoptera Response to Prescribed Burning

in the Texas Rolling Plains.

Abstract

Prescribed rangeland fire is a common land management practice, typically implemented in the spring, to remove dead vegetation, reduce woody plant abundance, and improve forage palatability for livestock in the Texas

Rolling Plains region. I examined the effects of spring prescribed fire on adult plant-dwelling Orthoptera species. Orthoptera are important consumers of plant material in rangelands and in large numbers can lead to reductions in total forage available for livestock and wildlife. In contrast, Orthoptera are also an important food source for many bird and mammal species. This study took place on 2 private ranches and one public wildlife management area in north-central Texas.

Burned and unburned sites were sampled using sweep nets and Orthoptera were identified to species. I examined Orthoptera diversity in response to spring prescribed fire and used model-based multivariate methods to examine species response to prescribed fire treatment, vegetation visual obstruction, and percent live vegetation. I also examined Orthoptera abundance in relation to the interaction of functional feeding group and fire treatment. I did not find a significant difference of spring burn treatment on Orthoptera diversity or assemblage composition. I did find a significant influence of percent live vegetation on my assemblage composition and a difference in functional feed 73

Texas Tech University, Britt Smith, May 2018 group abundance. These results suggest spring prescribed fires may not affect

Orthoptera species abundance several months post fire and that factors influencing living vegetation, such as drought, may have greater impact.

Introduction

Orthoptera are economically and ecologically significant insects.

Consumption of rangeland forage by grasshoppers in the western United States has been estimated at 13 million metric tons per year (Hewitt and Onsager 1983).

Orthoptera are also important insect prey for many vertebrate and invertebrate species (Greathead 1963, Fowler et al. 1991, Oedekoven and Joern 1998).

Because of their economic and ecological importance, Orthopterea are an intensively studied group of organisms. A significant body of research has been conducted on the influence of rangeland fire on grasshoppers (Hurst 1970, Evans

1984, Evans 1988, Porter and Redak 1996, Vermeire et al. 2004, Nadeau et al.

2006, Jonas and Joern 2007); however, most of the studies examining fire effects on Orthoptera communities have been conducted outside of Texas and outside of mesquite dominated rangelands, so little is characterized about fire effects on

Orthoptera in this region. Given that other insect taxa show variable responses to wildland fire interacting with regional and local characteristics, examining

Orthopteran response to prescribed fire within an ecoregion is important for making locally relevant management decisions.

In mixed-grass prairie of northwestern Oklahoma, fire did not have a significant influence on total biomass or abundance of grasshoppers (Vermire et al. 2004). However, at the species level, the abundance of a spur-throated

Texas Tech University, Britt Smith, May 2018 grasshopper, Hesperotettix viridis Scudder, was significantly lower in burned sites likely due to damage of its host plants (Asteraceae). The abundance of a slant-faced grasshopper, Ageneotettix deorum Scudder, also declined; this species lays eggs near the soil surface and may be particularly vulnerable to direct flame mortality (Vermier et al. 2004). As seen in H. viridis, Orthoptera that rely on specific host plant resources may be attracted or repelled from a burned area where fire has affected a host plant or its population (Chapman 1990). Further, protein is an important macronutrient for Orthopteran development, though field studies on the importance of plant nitrogen to Orthoptera diet selection are conflicting (Chapman 1990); grass regrowth after prescribed fire has higher crude protein content than grasses from unburned areas (Allred et al. 2011).

Further, changes in plant secondary compounds have also been shown to influence plant palatability for Orthoptera (Chapman 1990). Finally, the development of plant secondary compounds requires nutrients that can be influenced by fire (Lynds and Baldwin 1998).

A long-term study in a Kansas tallgrass prairie found no difference in grasshopper abundance among fire frequencies (Jonas and Joern 2007). They found that time since last fire was more important than long-term fire frequency in determining the abundance of grasshoppers (Jonas and Joern 2007). Further, they showed that yearly shifts in grasshopper community composition can be explained by local weather events (Jonas and Joern 2007). Another study at the same location found that grasshopper species richness increased with heterogeneity in vegetation structure and plant species richness (Joern 2005).

The same study also found grasshopper species richness decreased as canopy

Texas Tech University, Britt Smith, May 2018 height and total grass biomass increased, but forb biomass did not significantly influence grasshopper species richness (Joern 2005).

Orthoptera population dynamics are complex and incorporate such factors as growth, survival, reproduction, and dispersal. The use of fire in rangelands has the potential to influence each of these factors through direct and indirect effects.

Direct effects include damage of soil deposited eggs (e.g., A. deorum; Vermier et al. 2004), as well as mortality of nymphs and adults (Bock and Bock 2001).

Indirect effects include changes to vegetation structure and plant resources that may influence factors such as predation, food resources, and microclimate (Hurst

1970, Branson et al. 2006). Avian predators remove up to 25% of adult grasshoppers during summer months (Joern and Gaines 1990), and fires reduce the amount of plant litter and dead vegetation, which could act as refugia from predators. Further, pyric herbivory creates a strong focal disturbance in recently burned areas that can lead to strong differences in vegetation structure between unburned areas and burned and grazed areas (Fuhlendorf et al. 2009).

I sought to evaluate plant-dwelling Orthoptera responses to spring prescribed fire in the Rolling Hills region of north-central Texas. Since previous studies have shown declines in abundance of Orthoptera species on burned sites and because fire causes mortality to eggs and individuals, I hypothesize that abundance of Orthoptera species will be reduced on spring burned sites compared to unburned sites. However, since Orthoptera are generally highly mobile and vegetation within recently burned areas tends to have high protein content, Orthoptera species abundance may be similar to or greater than unburned areas. I examined my hypothesis using model-based approaches that

Texas Tech University, Britt Smith, May 2018 included treatment effects, vegetation visual obstruction, and percent live vegetation at sites.

Materials and Methods

Study Sites

My experiment took place in 2014 and 2015 on two private ranches and one public wildlife management area in the rolling plains ecoregion of northcentral Texas (Fig. 2.1). Private ranch 1 (PR1), sampled in 2014 only, was located in Archer County, Texas (33.57 N, 98.71 W; Fig. 2.2), and private ranch 2

(PR2), sampled in 2014 and 2015, was located in Dickens County, Texas (33.42

N, 100.88 W; Fig. 2.3). I conducted three prescribed fires at PR1 on 11 April 2014.

My prescribed burn units ranged from 1-2 ha in size. At this ranch, the 30 year mean precipitation and temperature are 780 mm and 17.6˚C, respectively

(NOAA-NCDC 2018). Soils in sampling sites at PR1 ranged from fine sandy loam to clay loam (Soil Survey Staff 2018). Cattle grazing is common at PR1 though cattle were deferred from my study sties. PR2 experienced a 12 ha wildfire between 4-11 May 2014, and on 10 April 2015, I conducted three prescribed fires

(24, 33, and 50 ha). PR2’s 30-year mean annual precipitation and temperature are 577 mm and 16.2˚C, respectively (NOAA-NCDC 2018). Sampling sites at PR2 consisted of clay loam soils (Soil Survey Staff 2018). Cattle were absent from the pasture that experienced wildfire in 2014 but were present during 2015. I also sampled at Matador Wildlife Management Area (WMA) in 2015 only, located in

Cottle County, Texas (34.11 N, 100.35 W; Fig. 2.4), and is owned and operated by the Texas Parks and Wildlife Department. Three prescribed fires were conducted

Texas Tech University, Britt Smith, May 2018 by Texas Parks and Wildlife at WMA between 11-27 March 2015. The prescribed fires were 21, 372, and 2314 ha in size. Thirty year mean annual precipitation and temperature are 633 mm and 17.2˚C (NOAA-NCDC 2018). Soils within sampling sites at MWA ranged from very fine sandy loam to loamy fine sand (Soil Survey

Staff 2018). Cattle were grazed in unburned pastures at WMA, but not in recently burned pastures.

Sampling sites on ranches were established in areas with flat topography, easy access, contained similar soil types, and distances greater than 440 m from adjacent sampling sites. Each site contained paired burned and unburned treatment areas. Within each treatment I established two parallel 100 m sweep net transects spaced 50 m apart. Sampling was conducted from 25 July to 18

August 2014 and 11-31 July 2015.

Grasshopper Sampling

I sampled plant-dwelling Orthoptera using sweep netting along two established 100 m transects. Sampling took place between 1000 and 1700 hours, and I sampled every 3 – 5 days during the sampling period. To collect

Orthoptera, a 30.5 cm diameter sweep net was passed through vegetation just above the soil surface in a 180-degree arc per step while walking along the transect (Samways et al. 2010). Net contents were emptied every 50 m into a 3.78

L resealable plastic bag with a cotton ball containing ethyl acetate. Orthoptera samples were stored in an Idylis model ICM070LC freezer at -5 ˚C until identification. Adult Orthoptera were counted and identified to species when possible using readily available taxonomic keys (e.g., Coppock 1962, Otte 1981,

1984, Capinera et al. 2004).

Texas Tech University, Britt Smith, May 2018

Analysis

All data were analyzed using the program R (R Core Team 2018). I compared my model covariates, vegetation visual obstruction and percent live vegetation, between burn and unburned treatments using a Welch’s t-test. For covariates, I set the significance level cutoff at 0.05. I examined species richness and Shannon diversity of my plant-dwelling Orthoptera for possible treatment differences using Welch’s t-test. I ensured sampling was sufficient for analysis by plotting a species accumulation curve using randomly added sites with 100 permutations via the vegan packages version 2.4-4 (Oksanen et al. 2017; Fig. 4.1).

To examine the response of the Orthoptera assemblage to burning and environmental covariates, I conducted a model-based multivariate analysis. Rare

Orthoptera species (those containing less than 5 individuals) were removed from analysis. Model-based multivariate methods are relatively new and provide some benefits over more traditional distance-based multivariate methods. For example, an important benefit is the ability to account for the mean-variance relationship that exists with multivariate data, whereby a study can have high species counts in a few sample sites and zero in several others. Further, these methods allow us to examine diagnostic plots to ensure the validity of my model in relation to my observed data (Warton et al. 2015). I analyzed Orthoptera species counts using a generalized linear model with a negative binomial distribution. In addition, I examined the response of individual species to my treatments and environmental covariates using post-hoc univariate tests adjusted for multiple comparisons. My additive model consisted of burn and unburned treatments, vegetation visual obstruction, and percent live vegetation. I analyzed

Texas Tech University, Britt Smith, May 2018 my data using the function anova.manyglm in the package mvabund version

3.12.3 in R (Wang et al. 2012). Model assumptions were examined to ensure the mean-variance relationship had a quadratic correlation and my plot of residuals compared to fitted values did not contain obvious patterns. For visualization of these data, I created a nonmetric multidimensional scaling (NMDS) ordination plot using Bray-Curtis distance with 2 dimensions. To make this ordination, I used functions metaMDS and envfit in the package vegan in R (Oksanen et al.

2017). I also examined functional feeding guilds for burn treatment effects.

Orthoptera species were placed into groups based on whether they fed on forbs/shrubs, graminoids, were carnivorous, or had generalist, mixed feeding strategy. I conducted my analysis using model-based multivariate methods with environmental factors of burn treatment and covariates of vegetation visual obstruction and percent live vegetation.

Results

I identified 1103 individuals Orthoptera from 29 distinct species (Table

4.1). I found no effect of burning on species richness (t = 0.14, df = 21.6, P =

0.89), Shannon diversity (t = -0.15, df = 20.02, P = 0.88), and Jaccard evenness

(t = -1.10, df = 14.02, P = 0.29). My most abundant species was A. deorum

Scudder with 181 individuals followed by Mermiria bivittata Serville and

Melanoplus glaucipes Scudder with 154 and 123, respectively (Table 4.1). I also found no significant treatment differences between environmental covariates, vegetation visual obstruction (t = 1.67, df = 20.63, P = 0.11) or percent live vegetation (t = 0.007, df = 19.95, P = 0.99, Fig. 2).

Texas Tech University, Britt Smith, May 2018

Model results showed a significant influence of percent live vegetation on

Orthoptera composition (df = 1,18, dev = 95.37, P = 0.007). I did not detect a significant difference between treatments (df = 1,20, dev = 39.98, P = 0.26) or vegetation visual obstruction (df = 1,19, dev = 51.55, P = 0.10) on Orthopteran assemblage (Table 4.2). Further, examining Orthoptera species using univariate tests adjusted for multiple comparisons, I did not observe a significant difference related to treatments, vegetation visual obstruction, or percent live vegetation.

NMDS ordination results show how sites, species, and environmental variables are related to each other and suggest an influence of percent live vegetation (Fig.

4.2). I did not find an influence of burn treatment or vegetation visual obstruction on functional feeding groups. However, I did find a significant positive influence of percent live vegetation on each of the functional feeding guilds (Table 4.3; Fig. 4.3).

Discussion

I did not observe a direct effect of post-fire burn treatment on plant- dwelling Orthopteran species, though I did see a significant influence of percent live vegetation on the assemblage. Diversity metrics for Orthoptera were not significantly different between burn treatments. Environmental covariates of vegetation visual obstruction and percent live vegetation also did not significantly differ between burn treatments. These results are consistent with the NMDS ordination, which shows species, sites, and environmental factors and covariates being scattered uniformly. I also did not see an effect of treatment on Orthoptera

Texas Tech University, Britt Smith, May 2018 functional feeding groups, though I did see an influence of live vegetation on all feeding guilds.

In the NMDS ordination, I observed two sites that are disjunct from all other sites. These two sites occurred on PR2 in 2014 which experienced a wildfire. Comparing these two sites to sites sampled in 2015 at PR2, few

Orthoptera individuals were captured in 2014. Only a single individual was captured in the unburned site and only 2 individuals in the burned area. In 2015 at PR2, captured Orthoptera individuals ranged from 10 – 23 individuals. An explanation for this low capture abundance may be due to lower precipitation in

2014 (517 mm) compared to 2015 (850 mm) at this site. Also, the NMDS ordination shows the other 4 sites from 2014 clumped together at the bottom of

NMDS axis 2. These 4 sites all occurred at PR1 and was the farthest east study ranch, which has greater average rainfall.

My study paralleled another similar study located on sand sage brush rangelands of western Oklahoma (Vermeire et al. 2004). These studies share two abundant species in common, A. deorum and M. bivittata. In my study these species are ranked 2nd and 3rd, and in Vermeire et al. 2004 they rank 2nd and

4th, respectively. Overall, our studies share 8 species in common. In Vermeire’s study researchers found fire-induced changes to vegetation that reduced the abundance of H. viridis. I captured 18 H. viridis in my study, which occurred mostly in unburned sites (n = 14) compared to burned sites (n = 4). However, I did not find a significant treatment effect. One possible reason could be my total number of replicates and low total abundance compared other Orthoptera in my study. Grasshopper A. deorum, the most abundant species in my study, was

Texas Tech University, Britt Smith, May 2018 captured in Vermeiere et al. 2004 at lower abundances after fall prescribed burns compared to unburned sites. However, Vermeire et al. 2004 did not find a significant difference in A. deorum between unburned and spring burned sites.

All sites in my study were burned in spring and therefore my results are similar.

Vermeire et al. 2004 found no significant difference for Melanoplus bowditchi/flavidus Scudder between burn treatments. I captured 60 M. bowditchi/flavidus, with 33 of those individuals being at a single site and also did not see a significant difference between treatment though this may be resulting from their locally patchy distribution. Overall, my results are similar when comparing spring burned sites to unburned.

While this study did not observe an effect of burning on Orthoptera species, I did find a significant influence of percent live vegetation on Orthoptera species composition. Most of the Orthoptera had a mixed feeding strategy or were graminoid obligate feeders. This is important as cattle are the main grazing animal in this region of Texas, whose diets are mostly comprised of grasses. If

Orthoptera abundances become great, this can impact total forage available for livestock and other wildlife species (Hewitt and Onsager 1983). It is possible that with more replicated treatments, a difference in burn treatment may have shown significance due to increased statistical power. In contrast, my sampling effort occurred several months after prescribed burns and since Orthoptera are highly mobile, they may have moved in or out of treatment sites. Future studies examining Orthoptera across the growing season and examining burn patch size or distance to unburned edge may lead to interesting conclusions. Centroids of large burn patches may not become repopulated as quickly due to increased

Texas Tech University, Britt Smith, May 2018 distance from unburned edge. In summary, in the Texas Rolling Plains region, spring prescribed fire does not appear to impact adult Orthoptera abundances.

Literature Cited

Allred, B.W., Fuhlendorf, S.D., Engle, D.M., Elmore, R.D., 2011. Ungulate preference for burned patches reveals strength of fire-grazing interaction. Ecology and Evolution 1, 132–144.

Bock, C.E., Bock, J.H., 1991. Response of grasshoppers (Orthoptera: Acrididae) to wildfire in a southeastern Arizona grassland. American Midland Naturalist 125, 162.

Branson, D.H., Joern, A., Sword, G.A., 2006. Sustainable management of insect herbivores in grassland ecosystems: new perspectives in grasshopper control. BioScience 56, 743–755.

Capinera, J.L., Scott, R.D., Walker, T.J., 2004. Field guide to grasshoppers, crickets, and katydids of the United States. Cornell University Press.

Chapman, R.F., Joern, A., 1990. Biology of Grasshoppers. John Wiley & Sons.

Coppock, S., 1962. Grasshoppers of Oklahoma (Orthoptera: Acrididae). M.S. Thesis: Oklahoma State University, Stillwater, OK.

Evans, E.W., 1984. Fire as a natural disturbance to grasshopper assemblages of tallgrass prairie. Oikos 43, 9–16.

Evans, E.W., 1988. Community dynamics of prairie grasshoppers subjected to periodic fire: predictable trajectories or random walks in time? Oikos 52, 283–292.

Fowler, A.C., Knight, R.L., George, T.L., McEwen, L.C., 1991. Effects of avian predation on grasshopper populations in North Dakota grasslands. Ecology 72, 1775–1781.

Texas Tech University, Britt Smith, May 2018

Fuhlendorf, S.D., Engle, D.M., Kerby, J., Hamilton, R., 2009. Pyric herbivory: rewilding landscapes through the recoupling of fire and grazing. Conservation Biology 23, 588–598.

Greathead, D.J., 1963. A review of the insect enemies of Acridoidea (Orthoptera). Transactions of the Royal Entomological Society of London 114, 437–517.

Hewitt, G.B., Onsager, J.A., 1983. Control of grasshoppers on rangeland in the united states: a perspective. Journal of Range Management 36, 202–207.

Joern, A., Gaines, S.B., 1990. Population dynamics and regulation in grasshoppers, in: Chapman, R.F., Joern, A. (Eds.), Biology of grasshoppers. Wiely-Interscience, New York, pp. 415–482.

Joern, A., 2005. Disturbance by fire frequency and bison grazing modulate grasshopper assemblages in tallgrass prairie. Ecology 86, 861–873.

Jonas, J.L., Joern, A., 2007. Grasshopper (Orthoptera: Acrididae) communities respond to fire, bison grazing and weather in North American tallgrass prairie: a long-term study. Oecologia 153, 699–711.

Lynds, G.Y., Baldwin, I.T., 1998. Fire, nitrogen, and defensive plasticity in Nicotiana attenuata. Oecologia 115, 531–540.

Nadeau, L., Cushing, P.E., Kondratieff, B.C., 2006. Effects of fire disturbance on grasshopper (Orthoptera: Acrididae) assemblages of the Comanche National Grasslands, Colorado. Journal of the Kansas Entomological Society 79, 2–12.

NOAA-NCDC, 2018. Climate Data Online. https://www.ncdc.noaa.gov/cdo-web/ (accessed 11 January 2018).

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Oedekoven, M.A., Joern, A., 1998. Stage-based mortality of grassland grasshoppers (Acrididae) from wandering spider (Lycosidae) predation. Acta Oecologica 19, 507–515.

Otte, D., 1981. The North American grasshoppers: Acrididae: Gomphocerinae and Acridinae. Harvard University Press.

Otte, D., 1984. The North American grasshoppers: Acrididae: Oedipodinae. Harvard University Press.

Porter, E.E., Redak, R.A., 1996. Short-term recovery of the grasshopper communities (Orthoptera: Acrididae) of a California native grassland after prescribed burning. Environmental Entomology 25, 987–992.

R Core Team, 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Samways, M.J., McGeoch, M.A., New, T.R., 2010. Insect conservation: a handbook of approaches and methods. Oxford University Press.

Soil Survey Staff. 2018. Web Soil Survey. Available online at the following link: https://websoilsurvey.sc.egov.usda.gov/. (accessed 11 January 2018).

Vermeire, L.T., Mitchell, R.B., Fuhlendorf, S.D., Gillen, R.L., 2004. Patch burning effects on grazing distribution. Rangeland Ecology & Management 57, 248–252.

Wang, Y., Naumann, U., Wright, S.T., Warton, D.I., 2012. mvabund - an R package for model-based analysis of multivariate abundance data: The mvabund R package. Methods in Ecology and Evolution 3, 471–474.

Warton, D.I., Foster, S.D., De’ath, G., Stoklosa, J., Dunstan, P.K., 2015. Model- based thinking for community ecology. Plant Ecology 216, 669–682.

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Tables and Figures

Table 4.1 Relative abundance and counts for Orthopteran species with counts greater than 5. Species Count Relative Abundance Ageneotettix deorum 181 0.1641 Mermiria bivittata 154 0.1396 Melanoplus glaucipes 123 0.1115 Conocephalus strictus 65 0.0589 Syrbula admirabilis 62 0.0562 Melanoplus bowditchii/flavidus 60 0.0544 Melanoplus angustipennis 58 0.0526 Melanoplus bispinosus 47 0.0426 Melanoplus lakinus 41 0.0372 Opeia obscura 41 0.0372 Melanoplus differentialis 40 0.0363 Campylacantha olivacea 31 0.0281 Melanoplus packardii 21 0.0190 Trachyrhachys kiowa 21 0.0190 Amphitornus coloradus 19 0.0172 Hesperotettix viridis 18 0.0163 Neobarrettia victoriae 17 0.0154 Boopedon nubilum 15 0.0136 Hesperotettix speciosus 12 0.0109 Oecanthus argentinus 11 0.0100 Pseudopomala brachyptera 10 0.0091 Aeoloplides turnbulli 9 0.0082 Conocephalus fasciatus 9 0.0082 Schistocerca lineata 8 0.0073 Hippiscus ocelote 7 0.0063 Spharagemon equale 7 0.0063 Phoetaliotes nebrascensis 6 0.0054 Arphia simplex 5 0.0045 Melanoplus sanguinipes 5 0.0045

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Table 4.2 Model results from treatment and environmental covariates on plant-dwelling Orthoptera species. Variable Res. d.f. d.f. diff dev P-value Intercept 21 Treatment 20 1 39.98 0.261 Vegetation Visual Obstruction 19 1 51.55 0.1 Percent Live Vegetation 18 1 95.37 0.005

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Table 4.3 Model results from treatment and environmental covariates on plant-dwelling Orthopteran functional feeding guilds. Relative Feeding Guild Unburned Treatment Visual Obstruction Live Vegetation Abundance coef dev P coef dev P coef dev P Carnivore 0.15 0.19 0.04 0.9 -0.01 1.26 0.62 0.1 6.09 0.04 Forbivore 0.07 -1.53 2.56 0.42 -0.21 0.03 0.89 0.1 6.89 0.04 Graminoids 0.49 -0.49 0.15 0.9 -0.1 1.53 0.62 0.04 7.59 0.04 Mixed 0.41 -0.39 0.52 0.81 -0.09 0.19 0.89 0.06 11.82 0.01

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Figure 4.1 Species accumulation curve with 95% confidence interval.

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Figure 4.2 NMDS ordination of plant-dwelling Orthoptera showing species and sites with fitted treatment groups and environmental factors (burned and unburned) and covariates (vegetation visual obstruction [VO] and percent live vegetation[LV]). Burned (0.001, -0.012) and unburned (0.006, 0.013) factors share similar coordinates and thus only unburned is shown on this ordination. Sites are coded by year with closed circles representing 2014 and closed triangles representing 2015. Species are represented by letters (A = Aeoloplides turnbulli, B = Ageneotettix deorum, C = Amphitornus coloradus, D = Arphia simplex, E = Boopedon nubilum, F = Campylacantha olivacea, G = Conocephalus fasciatus, H = Conocephalus strictus, I = Hesperotettix speciosus, J = Hesperotettix viridis, K = Hippiscus ocelote, L = Melanoplus angustipennis, M = Melanoplus bispinosus, N = Melanoplus bowditchii/flavidus, O = Melanoplus differentialis, P = Melanoplus glaucipes, Q = Melanoplus lakinus, R = Melanoplus packardii, S = Melanoplus sanguinipes, T = Mermiria bivittata, U = Neobarrettia victoriae, V = Oecanthus argentinus, W = Opeia obscura, X = Phoetaliotes nebrascensis, Y

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= Pseudopomala brachyptera, Z = Schistocerca lineata, AA = Spharagemon equale, BB = Syrbula admirabilis, CC = Trachyrhachys kiowa)

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Figure 4.2 NMDS ordination of plant-dwelling Orthoptera feeding guilds and sites with fitted treatment groups and environmental factors (burned and unburned) and covariates (vegetation visual obstruction [VO] and percent live vegetation). Burned (0.002, -0.001) and unburned (-0.001,- 0.001) factors share similar coordinates and thus only burned is shown on this ordination. Sites are coded by year with closed circles representing 2014 and closed triangles representing 2015.

Texas Tech University, Britt Smith, May 2018

CHAPTER 5

Prescribed Fire Effects on Rangeland Dung Beetles in the

Southern Great Plains (Coleoptera: Scarabaeinae,

Aphodiinae)

Abstract

Rangeland dung beetles represent an important assemblage of insects for the Great Plains. In this study, I examine the effects of a post-fire rangeland environment on a dung beetle assemblage in north-central Texas. I deployed baited pitfall traps to examine spring prescribed fire treatment, differences in vegetation visual obstruction, and dung density influence on dung beetle abundance and community composition. Using model-based multivariate methods, I did not find an influence of prescribed burning on the dung beetle assemblage. I report a negative influence of vegetation visual obstruction and no significant influence of dung density on dung beetle assemblages. These results suggest that prescribed fire may not negatively affect dung beetle species within the North American Great Plains; however, vegetation structure correlated to post-fire rangeland environments may influence local abundance.

Introduction

Dung beetles (Coleoptera: Scarabainae, Aphodiinae) are an ecologically and economically important insect group in rangeland ecosystems. Their life history assists in reducing livestock parasites and recycling fecal nutrients into

Texas Tech University, Britt Smith, May 2018 the soil (Fincher, 1973; Fincher, 1990; Nichols et al., 2008). Dung beetles are also an important food source for many other species, including burrowing owls that attract beetles using dung and feed the beetles to their offspring (Levey et al.,

2004). Understanding the influence of rangeland disturbances on dung beetle abundance and assemblage dynamics is important due to key roles these beetles play in soil health. Fire is a prominent and essential rangeland disturbance and may affect dung beetle populations through direct heat and ash related mortality, the intensive agricultural use of these environments, and indirect changes to habitat. While several studies report coarse-scale insect community patterns, few examine species-level insect responses, and only a handful have examined how dung beetles respond to wildland fire (e.g., Blanche et al., 2001; Swengel, 2001;

Engle et al., 2008).

Within North America, few studies have examined the influence of fire on dung beetle populations. On a rangeland enrolled in the Conservation Reserve

Program in western Texas, scarab beetles (family: Scarabaidae) were not influenced by spring prescribed fires (Davis, 1998). In Jalisco, Mexico, necrotraps located in burned forest contained more Onthophagus spp. compared to unburned forest (Rivera-Cervantes and Garcia-Real, 1998); however, this study was preliminary and contained a single replicate. In contrast, a study in

Tlaxcala, Mexico found more dung beetles in burned and edge forest sites compared to unburned forest sites (Arellano and Castillo-Guevara, 2014), but this trend was not significant. Outside North America, studies examining habitat and dung beetle populations have found varying responses of populations to

Texas Tech University, Britt Smith, May 2018 habitats altered by fire or other disturbance (Andrade et al., 2014; Barbero et al.,

1999; Louzada et al., 2010).

Direct mortality from rangeland fire on dung beetles has not been investigated. However, studies examining the thermal physiology of dung beetles have been found elevational gradients with maxima for dung beetles range around 52˚C at lower elevations (600m) to 45˚C at higher elevations (3000m;

Chown, 2001). While these temperatures are well below those produced by fires, some microhabitats experience greater variations in temperatures (e.g., Verble,

2012), and the temperature 2 cm under the soil may be relatively close to ambient. Thus, slight differences in critical thermal maxima may be important for survival. In addition, post-fire landscapes may be warmer due to black body effects and decreased shading (Wright and Bailey, 1982), so individuals with higher thermal maxima may be at a competitive advantage in post-fire environments.

Indirect factors influencing dung beetle use of burned or unburned areas have been minimally examined and may be locally influenced by several endogenous (e.g., soil type or habitat) and exogenous factors (e.g., fire season, climate, or weather). Some dung beetle species are dependent on soil type because they bury their dung balls under the soil surface and tunnel into the soil

(Nealis, 1977). Further, the type of animal dung located in an area can influence the composition and abundance of dung beetle species (Fincher et al., 1970). In general, dung beetles are most attracted to omnivores, carnivores, and herbivores in descending order of preference (Fincher et al., 1970; Whipple and Hoback,

2012). These edaphic and dung preferences have the potential to influence dung

Texas Tech University, Britt Smith, May 2018 beetle species occurrence, abundance, distribution, and site selection. For example, recently burned rangeland is attractive to grazing ungulates

(Fuhlendorf et al., 2009; Allred et al., 2011), and thus the concentration of grazing animals on burned patches may lead to an increase in dung which is attractive to dung beetles. Further, quality of the dung found on recently burned areas may have better nutrient characteristics for dung beetles because of high nitrogen content of forage consumed by grazing animals (Allred et al., 2011).

Vegetation structure and cover may also indirectly influence dung beetle response to burning (Nealis, 1977). Dense vegetation could influence a dung beetle’s locomotion, ability to create and move dung balls, and olfactory detection of dung (Nealis, 1977). Vegetation litter, which is typically consumed during a prescribed burn, can influence soil moisture, which may influence the ability of dung beetles to tunnel or bury dung balls. Alternatively, a burned and grazed site containing short vegetation may provide an easier terrain to roll dung balls to their resting spot.

My objective was to evaluate dung beetle (subfamilies: Aphodiinae and

Scarabaeinae) assemblage responses to prescribed fire through baited pitfall sampling. Since recently burned areas tend to have less total plant biomass and high concentrations of grazing animals eating highly palatable forage, I hypothesize that baited pitfall traps in recently burned sites will have more dung beetle taxa and individuals compared to unburned sites. I included the covariates of vegetation visual obstruction and dung density at trapping sites, because rangeland fire consumes dead biomass and grazing animals preferentially graze on recent burns (Fuhlendorf and Engle, 2004; Eby et al., 2014). Further, dung

Texas Tech University, Britt Smith, May 2018 beetles use olfactory cues to locate dung, which presumably is more detectable in open habitats where wind currents are less restricted by trees; however, in contrast, smaller dung beetles may be inhibited by these stronger winds.

Methods

Study Area

I sampled dung beetle abundance at Matador Wildlife Management Area

(WMA; Fig. 5.1), Paducah, TX (34°07' N, 100°20' W, elevation 523m). Matador

WMA is owned and operated by the Texas Parks and Wildlife Department and is situated in the Texas Rolling Plains ecoregion. The dominant vegetation type is mixed-grass mesquite rangelands (Wright and Bailey, 1982). Common plant species include honey mesquite (Prosopis glandulosa Torr.), redberry juniper

(Juniperus pinchotii Sudw.), blue grama (Bouteloua gracilis (Willd. ex Kunth)

Lag. ex Griffiths), and sand dropseed (Sporobolus cryptandrus (Torr.) A. Gray).

Soil texture at sampling sites ranged from very fine sandy loam to loamy fine sand (Soil Survey Staff, 2018). Thirty year mean annual precipitation is 633.0 mm, and mean annual temperature is 17.2˚C (NOAA-NCDC 2018). Precipitation during sampling was 0.0 mm for both years. Mean max temperature during sampling was 32.5˚C in 2015 and 34.3˚C in 2016. Cattle are actively managed at

Matador WMA. During the years of study, sample pastures that were not scheduled for prescribed fires had a stocking rates between 0.22 and 1.02 animal unit month (AUM) ha-1 with cow calf pairs. While baited pitfall traps were active, sample pastures containing cattle were stocked between 0.22 and 0.51 AUM ha-1.

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Cattle were annually treated with ivermectin parasiticide in the spring and had ad lib access to mineral blocks containing methoprene while in pastures.

Prescribed fires at Matador WMA were conducted from 11-27 March 2015 and 17-31 March 2016. Prescribed fires were approximately 21, 372, and 2314 ha in 2015 and 175, 488, and 975 ha in 2016. I selected sampling sites at Matador

WMA based on ease of access, similar soil types, lack of topography, and distance of at least 1 km from nearby sampling sites. Sampling sites consisted of paired burned and unburned treatments with baited pitfall traps located a minimum of

150 m from treatment edge. In 2015, I established six replicated paired sampling sites. In 2016, I established five replicated paired sampling sites and one unpaired burned sampling site, because the paired unburned treatment became inaccessible during sampling.

Trapping Methods

Baited pitfall trap sampling occurred from 16-22 June 2015 and 20-26

June 2016 at the Matador WMA. I placed two dung beetle pitfall traps per site per treatment (Samways et al., 2010). Traps within each treatment were spaced

150 m apart, and at least 300 m from any traps in neighboring treatments to reduce interference between traps (Larsen and Forsyth, 2005). Each trap consisted of two 1500 mL Tupperware containers buried with the rim flush to the soil surface. Inside the top container, 200 mL of water, 100 mL of propylene glycol, and a drop of non-scented dish washing detergent were mixed and used to trap and preserve dung beetles captured in the trap (Weeks and McIntyre, 1997;

Woodcock, 2005). A small, consistent amount of human dung (10-15 mL) wrapped in cheesecloth was placed on a steel rod that hung over the trap to

Texas Tech University, Britt Smith, May 2018 attract the dung beetles and prevent them from accessing the dung bait (Whipple and Hoback, 2012). Each dung beetle pitfall trap was covered with a Styrofoam plate anchored with sod staples to prevent rainfall from entering the trap, while allowing dung beetle access (Woodcock, 2005). Traps were rebaited every two days, emptied daily for six consecutive days, and contents were preserved in propylene glycol. Samples were identified to the highest taxonomic resolution possible using a Leica dissecting microscope in the laboratory (Halffter, 1961;

Ratcliffe and Paulsen, 2008). I deposited voucher specimens at the Museum of

Texas Tech University.

Vegetation Structure Estimation

Vegetation height and visual obstruction were sampled between 29 June and 29 July 2015 and 20 July and 25 July 2016. For each dung beetle sampling site, I sampled vegetation along two parallel 100 m transects spaced 50 m apart.

Vegetation structure was sampled using a modified Robel pole technique with a

150 cm Robel pole measured at 2 m from the pole and 1 m above the soil surface at 1 cm increments (Robel et al., 1970). Vegetation height was determined by measuring the tallest plant between the Robel pole and the observer. Visual obstruction was determined by measuring the lowest point on the Robel pole visible to the observer. A measurement was recorded from each of the four cardinal directions for 10 points spaced 10 m apart along each 100 m transect.

The measurements were averaged along the two transects to give a mean vegetation height and visual obstruction for each dung beetle sample site.

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Cattle Dung Density Estimation

Burned and unburned sites were sampled for the presence of dung.

Sampling was conducted along four parallel 100 m transects spaced 25 m apart within each treatment and located between my two pitfall traps. Dung that was observed while walking the transect was measured perpendicularly to the transect to obtain a distance from the transect (Krebs, 1999; Buckland et al.,

2005). Density was estimated by dividing the total dung counts in a sample area by the total length (400 m) of the sample transect multiplied by 4.6 m, which is twice the farthest distance dung was observed from the transect (2.3 m).

Analysis

Dung beetle abundance, richness, evenness, and Shannon diversity (H') were compared between burned and unburned treatment sites using a Welch's t- test in R version 3.4.3 (R Core Team, 2017). I examined species accumulation with sites added randomly using 100 permutations via the Vegan package version

2.4-4 in R (Oksanen et al., 2017; Fig. 5.2). Traps missing their dung bait upon collection were removed from analysis (n = 2). Dung beetles are attracted to carrion (Wipple and Hoback, 2012), but because I collected traps daily, analysis included traps containing dead vertebrates.

Assemblage analysis was conducted using model-based multivariate methods (Warton et al., 2015). Compared to distance-based methods, model- based methods provide advantages such as better interpretation of the observed data's relation to model parameters and accounting for the mean-variance relationship of multivariate data (Warton et al., 2015). I examined dung beetle

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Texas Tech University, Britt Smith, May 2018 taxa counts using a generalized linear model with a negative binomial distribution. I also conducted post hoc univariate tests adjusted for multiple comparisons using step-down resampling to examine individual taxa responses to environmental factors and covariates. I used the function anova.manyglm in the package mvabund version 3.12.3 in R (Wang et al., 2012). I checked the mean-variance assumption by ensuring the mean-variance relationship was quadratic. I checked the log-linearity assumption by examining residuals versus fitted values for unusual patterns (Dunn and Smyth, 1996). My model included treatment factors and covariates of vegetation visual obstruction and dung density. For visualization I conducted a nonmetric multidimensional scaling

(NMDS) using Bray-Curtis distances and a square root transformation followed by Wisconsin double standardization on the dung beetle taxa counts to reduce the influence of extreme values. I conducted my NMDS using the Vegan package in R.

Results

I identified 66634 individual dung beetles from 10 different taxa across both years. The most abundant dung beetle was Onthophagus pennsylvanicus

Harold with 40585 individuals represented, followed by Canthon pilularis

L./imitator Brown with 12947 individuals and Canthon (Boreocanthon) ebenus

Say with 5194 individuals. I saw no difference (t = 0.7179, df = 20.99, P = 0.481) in total abundance of dung beetles in traps between burned and unburned areas.

I also saw no differences in dung beetle species richness (t = -0.2585, df = 19.74,

P = 0.799), Shannon diversity (t = 0.1312, df = 20.42, P = 0.897), and evenness

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(Jaccard, t = 0.1512, df = 19.28, P = 0.811) in traps between burned and unburned areas. I found no difference among environmental covariates between treatments (Fig. 5.3).

Model analysis results show a significant influence of visual obstruction

(dev = 36.6, P = 0.02) on dung beetle taxa abundances, but no influence of treatment (dev = 7.3, P = 0.60) or dung density (dev = 22.2, P = 0.13) on overall dung beetle taxa captured in traps. Results from the post hoc univariate analysis show a significant negative influence of visual obstruction on Digitonthophagus gazella Fabricius (Table 5.1, Fig. 5.4). I also saw a significant negative influence of dung density on Onthophagus velutinus Horn (Table 5.1). The NMDS ordination also shows a strong influence of vegetation visual obstruction (Fig.

5.5). Further, when sites are coded by year, I observe a separation of sites along axis NMDS1.

Discussion

While I did not observe a direct effect of fire on dung beetle communities, changes in the post-fire environment influenced dung beetle assemblages via decreases in vegetation visual obstruction. Further, I observed individual taxa responses to covariates. Dung density within the sampling sites did not significantly influence my dung beetle assemblage. These findings are consistent with the results of my NMDS ordination which also showed a potentially strong influence of sampling year on taxa. I did not observe a difference in species richness, evenness, or Shannon diversity relative to treatments.

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It is well known that rangeland fires alter vegetation structure and grazing animal distributions (Fuhlendorf et al., 2004; Allred et al., 2011). The results of my analysis showed a negative influence of vegetation visual obstruction on the dung beetle assemblage and particularly on D. gazella, an introduced and widely distributed tunneling dung beetle. Since dung beetles locate dung pats through olfactory cues, I speculate that areas with low vegetation visual obstruction have increased ground-level wind speeds and thus may attract dung beetles from a farther distance (Wolfe and Nickling, 1993). I attempted to measure ground level wind speed between burned and unburned treatments using HOBO wind speed dataloggers, but my attempts were not successful due to small mammal interference (chewing on wires) and variations in measurements between anemometers. I feel this is an area that should be examined further. A design in which rangeland patches are mowed and maintained with short vegetation near patches with high vegetation visual obstruction could sufficiently examine this hypothesis. In addition, areas with low vegetation visual obstruction have greater soil surface temperatures due to reduced vegetation shading and increased solar radiation (Vermeire et al., 2005). Ectotherms are known to change their behavior and have different physiological tolerances to changes in habitat microclimate

(Stevenson, 1985). The dung beetle assemblage in this study may prefer the particular microclimate of areas with low vegetation visual obstruction.

My results did not indicate an influence of dung density on the dung beetle assemblage, but O. velutinus was found to be negatively associated with dung density. However, O. velutinus is also the rarest taxa encounter in the study with only 13 individuals being collected across both years and only three of six

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Texas Tech University, Britt Smith, May 2018 occupied sites contained observed dung. Most of the dung encountered along transects was cattle dung, but deer and small mammal dung was also present. I also observed that most of the dung found along transects appeared old (> 1 month) and dry, which can influence dung beetle recruitment (Edwards, 1991;

Gittings and Giller, 1998). The amount of moisture required by dung beetles to consume and manipulate the dung may also vary between the functional groups of rollers, tunnellers, and dwellers. Tunneling dung beetles reside under the dung pat, where moisture should be higher compared to the dung pat surface. In contrast, rollers that manipulate dung on the surface and move their brooding ball to another location, may require higher dung moisture content throughout the dung pat. Locally, the dung beetles attracted to traps may be influenced by the condition of available dung in the study sites.

In retrospect, a longitudinal study may have elicited more informed patterns of assemblage response that my study did not examine. I intensively examined a brief period to ensure capture of rare taxa and to remove time as a confounding variable. However, over time, other studies have observed differences in dung beetle abundance associated with weather or seasonality (de

Oca and Halffter, 1995). In my study the primary objective was to evaluate whether prescribed fire had an influence on dung beetle occurrence and distribution. Though I did not see an effect of fire directly, I did see an influence of visual obstruction which prescribed fire is known to influence.

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Implications

This study is the first to examine the post-fire rangeland environment’s influence on dung beetle assemblages in the United States. I did not observe an influence of fire treatments directly, but I saw a significant negative influence of vegetation visual obstruction on this dung beetle assemblage. In some rangeland management practices, such as patch burn grazing, that maximize vegetation structural heterogeneity, I may see high abundances of dung beetles on recently burned areas that are selectively grazed by ungulates due to low visual obstruction and increased dung density. Further, higher abundances of dung beetles should lead to greater competition with dung breeding flies, and lead to increased movement of dung into the soil. These benefits translate into economic incentives via increased forage production and decreased need for parasiticide application.

Literature Cited

Allred, B.W., Fuhlendorf, S.D., Engle, D.M., Elmore, R.D., 2011. Ungulate preference for burned patches reveals strength of fire-grazing interaction. Ecology & Evolution 1, 132–144.

Andrade, R.B. de, Barlow, J., Louzada, J., Vaz-de-Mello, F.Z., Silveira, J.M., Cochrane, M.A., 2014. Tropical forest fires and biodiversity: dung beetle community and biomass responses in a northern Brazilian Amazon forest. Journal of Insect Conservation 18, 1097–1104.

Arellano, L., Castillo-Guevara, C., 2014. Efecto de los incendios forestales no controlados en el ensamble de escarabajos coprófagos (Coleoptera: Scarabaeidae) en un bosque templado del centro de México. Revista

106

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Mexicana de Biodiversidad 85, 854–865.

Barbero, E., Palestrini, C., Rolando, A., 1999. Dung beetle conservation: effects of habitat and resource selection (Coleoptera: Scarabaeoidea). Journal of Insect Conservation 3, 75–84.

Blanche, K.R., Andersen, A.N., Ludwig, J.A., 2001. Rainfall-contingent detection of fire impacts: responses of beetles to experimental fire regimes. Ecological Applications 11, 86–96.

Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., 2005. Distance sampling, in: Encyclopedia of biostatistics. John Wiley & Sons Inc., Hoboken, NJ, USA.

Chown, S.L., 2001. Physiological variation in insects: hierarchical levels and implications. Journal of Insect Physiology 47, 649–660.

Davis, S.S., 1998. Effects of prescribed fire on small mammals and beetle assemblages in Conservation Reserve Program (CRP) grasslands. M.S. thesis. Texas Tech University. Lubbock, TX. de Oca T., E.M., Halffter, G., 1995. Daily and seasonal activities of a guild of the coprophagous, burrowing beetle (Coleoptera Scarabaeidae Scarabaeinae) in tropical grassland. Tropical Zoology 8, 159–180.

Dunn, P.K., Smyth, G.K., 1996. Randomized quantile residuals. Journal of Computational and Graphical Statistics 5, 236–244.

Eby, S.L., Anderson, T.M., Mayemba, E.P., Ritchie, M.E., 2014. The effect of fire on habitat selection of mammalian herbivores: the role of body size and vegetation characteristics. Journal of Animal Ecology 83, 1196–1205.

Edwards, P.B., 1991. Seasonal variation in the dung of African grazing mammals, and its consequences for coprophagous insects. Functional Ecology 5, 617– 628.

107

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Engle, D.M., Fuhlendorf, S.D., Roper, A., Leslie, D.M., Jr., 2008. Invertebrate community response to a shifting mosaic of habitat. Rangeland Ecology & Management 61, 55–62.

Fincher, G.T., 1973. Dung beetles as biological control agents for gastrointestinal parasites of livestock. The Journal of Parasitology 59, 396–399.

Fincher, G.T., Stewart, T.B., Davis, R., 1970. Attraction of coprophagous beetles to feces of various animals. The Journal of Parasitology 56, 378–383.

Fincher, G.T., 1990. Biological control of dung-breeding flies: pests of pastured cattle in the United States., in: Rutz, D.A, Patterson, R.S. (Eds.), Biocontrol of arthropods affecting livestock and poultry. Westview Press Inc., Colorado. pp. 137-151.

Fuhlendorf, S.D., Engle, D.M., 2004. Application of the fire–grazing interaction to restore a shifting mosaic on tallgrass prairie. Journal of Applied Ecology 41, 604–614.

Fuhlendorf, S.D., Engle, D.M., Kerby, J., Hamilton, R., 2009. Pyric herbivory: rewilding landscapes through the recoupling of fire and grazing. Conservation Biology 23, 588–598.

Gittings, T., Giller, P.S., 1998. Resource quality and the colonisation and succession of coprophagous dung beetles. Ecography 21, 581–592.

Halffter-Salas, G., 1961. Monografía de las especies Norteamericanas del género Canthon Hoffsg. (Coleoptera: Scarabaeidae). Ciencia 20, 225-320.

Krebs, C.J. 1989. Ecological Methodology. Harper & Row, New York.

Larsen, T.H., Forsyth, A., 2005. Trap spacing and transect design for dung beetle biodiversity studies. Biotropica 37, 322–325.

Levey, D.J., Duncan, R.S., Levins, C.F., 2004. Animal behavior: use of dung as a tool by burrowing owls. Nature 431, 39–39.

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Louzada, J., Lima, A.P., Matavelli, R., Zambaldi, L., Barlow, J., 2010. Community structure of dung beetles in Amazonian savannas: role of fire disturbance, vegetation and landscape structure. Landscape Ecology 25, 631–641.

Nealis, V.G., 1977. Habitat associations and community analysis of South Texas dung beetles (Coleoptera: Scarabaeinae). Canadian Journal of Zoology 55, 138–147.

Nichols, E., Spector, S., Louzada, J., Larsen, T., Amezquita, S., Favila, M.E., 2008. Ecological functions and ecosystem services provided by Scarabaeinae dung beetles. Biological Conservation 141, 1461–1474.

NOAA-NCDC, 2018. Climate Data Online. https://www.ncdc.noaa.gov/cdo-web/ (accessed 11 January 2018).

R Core Team, 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Ratcliffe B.C. Paulsen M.J. 2008. The Scarabaeoid beetles of Nebraska (Coleoptera: Scarabaeoidea). Bulletin of the University of Nebraska State Museum 22: 1–570.

Rivera-Cervantes, L.E., Garcia-Real, E., 1998. Analisis preliminar sobre la composicion de los escarabajos necrofilos (Coleoptera: Silphidae y Scarabaeidae) presentes en dos bosques de pino (uno danado por fuego), en la estacion cientifica las joyas, sierra de manatlan, Jalisco, Mexico. Dugesiana 5, 11–22.

Robel, R.J., Briggs, J.N., Dayton, A.D., Hulbert, L.C., 1970. Relationships between visual obstruction measurements and weight of grassland

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vegetation. Journal of Range Management 23, 295–297.

Samways, M.J., McGeoch, M.A., New, T.R., 2010. Insect conservation: a handbook of approaches and methods. Oxford University Press.

Soil Survey Staff. 2018. Web Soil Survey. Available online at the following link: https://websoilsurvey.sc.egov.usda.gov/. (accessed 11 January 2018).

Swengel, A.B., 2001. A literature review of insect responses to fire, compared to other conservation managements of open habitat. Biodiversity and Conservation 10, 1141–1169.

Verble, R., 2012. Effects of prescribed fire on Ozark ant ecology. Ph.D. Dissertation, University of Arkansas - Little Rock. Little Rock, Arkansas.

Vermeire, L.T., Wester, D.B., Mitchell, R.B., Fuhlendorf, S.D., 2005. Fire and grazing effects on wind erosion, soil water content, and soil temperature. Journal of Environment Quality 34, 1559-1565.

Wang, Y., Naumann, U., Wright, S.T., Warton, D.I., 2012. mvabund - an R package for model-based analysis of multivariate abundance data: The mvabund R package. Methods in Ecology and Evolution 3, 471–474.

Warton, D.I., Foster, S.D., De’ath, G., Stoklosa, J., Dunstan, P.K., 2015. Model- based thinking for community ecology. Plant Ecology 216, 669–682.

Weeks, R.D., McIntyre, N.E., 1997. A comparison of live versus kill pitfall trapping techniques using various killing agents. Entomologia Experimentalis et Applicata 82, 267–273.

Whipple, S.D., Hoback, W.W., 2012. A comparison of dung beetle (Coleoptera: Scarabaeidae) attraction to native and exotic mammal dung. Environmental Entomology 41, 238–244.

Wolfe, S.A., Nickling, W.G., 1993. The protective role of sparse vegetation in wind erosion. Progress in Physical Geography 17, 50–68.

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Woodcock, B.A., 2005. Pitfall trapping in ecological studies, in: Leather, S.R. (Ed.), Insect sampling in forest ecosystems. Blackwell Science Ltd, pp. 37– 57.

Wright, H.A., Bailey, A.W., 1982. Fire ecology: United States and southern Canada. John Wiley & Sons, Inc., Hoboken, NJ, USA.

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Tables and Figures

Table 5.1. Results from model-based multivariate analysis for environmental factors and covariates on individual taxa. Relative Species Abundance Unburned Treatment Visual Obstruction Dung Density coef dev P coef dev P coef dev P Aphodius spp. 0.0055 -0.13 0.27 0.95 -0.06 0.81 0.57 -34.89 1.72 0.79 Canthon (Boreocanthon) ebenus 0.1230 -0.72 0.68 0.93 -0.11 4.33 0.32 -27.76 1.69 0.79 Canthon pilularius/imitator 0.1943 -0.57 2.57 0.62 -0.06 5.05 0.26 -11.72 1.50 0.79 Phanaeus difformis 0.0188 -0.59 0.61 0.93 -0.07 2.78 0.46 -3.29 0.03 0.92 Euoniticellus intermedius* 0.0002 1.59 0.95 0.93 0.28 4.24 0.32 -36.59 0.25 0.92 Digitonthophagus gazella* 0.0087 -0.49 0.81 0.93 -0.17 10.99 0.02 -22.49 2.12 0.76 Melanocanthon nigricornis 0.0011 0.44 0.26 0.95 0.15 0.95 0.60 23.86 0.45 0.92 Onthophagus pennsylvanicus 0.6091 -1.03 0.26 0.95 -0.11 2.49 0.46 -17.68 0.70 0.92 Onthophagus velutinus 0.0002 -2.03 0.9 0.93 -0.20 1.72 0.50 -888.1 11.22 0.03 Canthon vigilans 0.0390 -0.71 0.02 0.95 -0.11 3.24 0.40 -31.58 2.51 0.71 * Introduced species

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Figure 5.1. Dung beetle sample sites located at Matador Wildlife Management area. Closed circles represent pitfall trap locations and color indicates year where blue = 2015 and red = 2016. Red circles with blue stars are pitfall trap sites that were sampled in both 2015 and 2016. Prescribed burn areas are indicated by colored polygons.

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Figure 5.2. Species accumulation curve with 95% confidence interval.

Figure 5.3. Environmental covariates compared between burn and unburn treatment factors. a) Mean ± SE vegetation visual obstruction (p = 0.097). b) Mean ± SE dung density between treatments in sample sites (p = 0.308).

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Figure 5.4. Comparison of log of Digitonthophagus gazella individuals at sites to vegetation visual obstruction with a fitted line ± SE. Fitted line: log(D. gazella) = 4.09 – 0.14(x)

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Figure 5.5. NMDS ordination showing species and sites with fitted treatment groups and environmental factors and covariates. Burned (-0.004, 0.002) and unburned (0.004, -0.002) factors share similar coordinates and thus only unburned is shown on this ordination. P. difformis (D, -0.1, 0.07) shares similar coordinates as B. ebenus (B, -0.08, 0.07) and is not shown. Sites have been color coded by year. Species are represented by letters (A = Aphodius spp., B = Canthon (Boreocanthon) ebenus, C= C. pilularis/imitator, D = Phaneus difformis, E = Euoniticellus intermedius, F = Digitonthophagus gazella, G = Melanocanthon nigricornis, H = Onthophagus pennsylvanicus, I = Othophagus velutinus, J = Canthon vigilans)

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CHAPTER 6

Post-Fire Summer Soil Surface Temperature in the Texas

High Plains.

Abstract

Fire is a common disturbance in rangelands of the southern High Plains.

Wildfires, both natural and anthropogenic, as well as prescribed fires occur regularly and influence habitat characteristics for the residing organisms; soil surface temperature is one microhabitat characteristic that can influence the spatial and temporal distributional patterns of animals. Recently burned areas have been shown to have higher daytime maximum temperatures and lower nighttime minimum temperatures compared to unburned mixed and tallgrass rangelands. Soil surface temperature in response to post-fire disturbance has not been examined in the southern High Plains. I sought to use a novel method employing iButton temperature data loggers to measure soil surface temperatures in year of burn, one-year post-burn, and unburned rangeland across the summer season in western Texas. I modeled soil surface temperature using harmonic regression and included fire treatment, hour, Julian date, Fourier terms, ambient air temperature, and solar radiation with a random site effect and a first-order autoregressive structure to account for serial correlation. My model also included an interaction of fire treatment and hour. I found a significant influence of all covariates on soil surface temperature. I also examined daily mean soil surface temperature variance. I found greater daily variance of soil surface temperature in year of burn and one year post burn sites compared to 117

Texas Tech University, Britt Smith, May 2018 unburned. Results show the same trend of higher daytime maximum temperatures and lower nighttime minimum temperatures on recently burned areas compared to unburned areas that are seen in mixed and tallgrass prairie.

Introduction

Wildland fire, wildfire, and prescribed fire are common in shortgrass rangelands of the southern Great Plains. Fire is a necessary regular disturbance that hinders woody plant and cactus encroachment into this grass-dominated ecosystem. Fires in shortgrass rangelands are driven by fuel abundance, fuel continuity, wind speeds, and relative humidity. Ignition sources are either from natural causes, predominately lightning, or anthropogenic sources (i.e. prescribed fire or wildfire due to unextinguished cigarette butts). Wildland fire in shortgrass rangelands can influence abiotic conditions and the greater biotic system.

Fire in rangelands can influence the soil surface temperatures in both short and long term (Wright and Bailey 1982). Short term, during and immediately after fire, effects include rapid rise in temperatures during the consumption of fuel and is influenced by the residence time of the fire (Wright and Bailey 1982). Once the flame has passed, black surface ash covers the ground and likely increases the absorption of solar radiation and emission of thermal infrared radiation. The temporal duration of this black surface ash is dependent on wind speeds and precipitation. Long-term effects of wildland fire also result in greater rangeland soil surface temperatures as higher amounts of bare soil exist due to fire’s consumption of litter and dead vegetation (Wright and Bailey 1982).

Wildland fire’s influence on post-fire soil surface temperature has been examined

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1972, Vermeire et al. 2005, Limb et al. 2009, Vermeire et al. 2011). However, this relationship has not been examined in shortgrass rangelands of the southwestern

Great Plains.

Changes to the post-fire rangeland environment influence the microclimate in recently burned areas. Abiotic characteristics influenced by wildland fire include soil surface temperature, evapotranspiration, and wind and water erosion (Wright and Bailey 1982). These factors can affect biotic components of the ecosystem, particularly plants and animals. When adequate soil moisture is present, the increase soil temperatures on recently burned areas stimulate nitrate production and causes perennial plants to emerge from dormancy earlier compared to those in unburned areas thus, leading to increased vegetation production (Sharrow and Wright 1977). Seed germination is also influenced by the post-fire environment as warmer soils lead to earlier germination, given that other conditions for germination are met (Call and

Roundy 1991). Animal distributions are also influenced by post-fire soil surface temperatures, particularly ectotherms. Reptiles can have narrow thermal optima and thermoregulate by selecting sites with preferred conditions (Sartorius et al.

2002). During colder periods of the year, burned areas can provide basking sites for ectotherms to increase body temperatures through solar gain during the midday, and thermal surface conduction. In contrast, during midday burned areas may exceed the animal’s thermal tolerance and cause them to seek refugia under litter or burrow into the soil. Insects are also known to respond similarly to thermal gradients in their environment (Chappell and Whitman 1990, Tuf et al.

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2012). These fluctuations in soil surface temperature are variable depending on time of day, season, and geographic location. Precocial bobwhite quail chicks are unable to regulate body temperature and are known to seek thermal refugia to avoid areas with low quantities of vegetation (Carroll et al. 2015). Post-fire soil surface temperatures can also influence endotherms. High summer soil surface temperatures are shown to negatively influence survival rates of banner-tailed kangaroo rats (Moses et al. 2012). Further, high daytime and low night time soil surface temperatures may limit the ability of thermal specialist organisms to inhabit recently burned areas.

I examined post-fire soil surface temperatures in shortgrass rangelands using iButton data loggers to measure soil surface temperature at one-hour intervals across summer 2017. Traditional methods for measuring soil surface temperature are thermocouples, thermal infrared gun, and soil temperature probes. Another study has found iButtons to be an effective method of measuring soil surface temperature between burned and unburned sites in Scotland (Grau-

Andrés et al. 2017), but given the limited examination in the literature, my first objective was to examine iButton measurements on burned and unburned sites compared to a thermal infrared gun and soil temperature probe. My second objective was to examine soil surface temperature across the summer season on a ranch that had experienced wildfires in recent years. I hypothesized iButtons would accurately measure soil surface temperature compared to traditional methods and that diurnal soil surface temperature would be greater on recently burned sites even though shortgrass rangeland tends to have a thinner litter layer compared to mixed and tallgrass rangeland.

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Methods

Study Area

I conducted this study in the Llano Estacado region of western Texas. This study included two study sites, Lubbock Lake Landmark (LLL, 33° 37' N, 101° 53'

W) and Private Ranch 1 (33° 22' N, 102° 33' W, Fig. 6.1). LLL is owned and operated by Texas Tech University, while PR1 is a privately owned and managed ranch. Plant species at LLL are typical shortgrass prairie species with abundant blue grama (Bouteloua gracilis (Willd. ex Kunth) Lag. ex Griffiths), buffalo grass

(Bouteloua dactyloides (Nutt.) J.T. Columbus), soapweed yucca (Yucca glauca

Nutt.), and honey mesquite (Prosopis glandulosa Torr.). PR1 contains similar plants, but also has mottes of sand shinnery oak (Quercus havardii Rydb.).

In 2016, I sampled LLL as a pilot study to examine if iButtons could be used to measure soil surface temperature. A lightning-ignited fire (8.2 ha) occurred at LLL on 27 July 2016 that was opportunistically sampled as well as a prescribed fire (4.7 ha) conducted on 14 July 2016. Thirty year mean precipitation and temperature at LLL is 485.6 mm and 15.9 ˚C, respectively

(NOAA-NCDC 2018). In 2016, the annual precipitation was 494.5 mm (West

Texas Mesonet 2018). Soils in sample areas at LLL ranged from loam to clay loam with 1 – 3% slopes (Soil Survey Staff 2018).

In 2017, I sampled two wildfires that occurred on PR1. The earliest wildfire occurred on 17 June 2016 (140 ha) and the more recent fire occurred 28

February 2017 (~5000 ha). Thirty year mean precipitation and temperature at

PR1 is 503.9 mm and 15.2˚C, respectively (NOAA-NCDC 2018). In 2016, the

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Texas Tech University, Britt Smith, May 2018 annual precipitation was 444.5 mm (West Texas Mesonet 2018). Soils at PR1 sampling area were all Patricia and Amarillo loamy fine sands with 0 – 3% slopes

(Soil Survey Staff 2018).

Sampling Methods

In my pilot study at LLL, I compared soil surface temperature measurements between iButtons (Maxim Integrated model DS1921G-F50), thermal infrared gun (Cole Parmer model 39650-20), and a soil temperature probe (Reotemp Instruments model TM99a). Sampling took place from 9 – 13

September 2016. Sampling sites (n = 11 per treatment) were generated using

QGIS version 2.18.8 within burned and unburned treatment areas (QGIS

Development Team 2017). At each sampling area, a single iButton was placed 0.5 cm under the soil surface. IButtons were set to record temperature every 15 minutes. On 9 September 2016 between 1500 and 1800 I measured soil surface temperature using a thermal infrared gun and soil surface temperature probe at each sampling site. I took 4 measurements 5 cm in each cardinal direction from the location of the iButton for each instrument per sampling site. The thermal infrared gun was held 10 cm above the soil surface, and the soil temperature probe was placed 0.5 cm under the soil surface. In unburned sites, I measured temperature soil surface temperature underneath the litter layer. On 9 September

2016 the maximum solar radiation was 893 Wm-2, the maximum 2 m ambient temperature was 33.2˚C, and cloud cover was 0% (West Texas Mesonet 2018).

IButtons were collected and data downloaded on 13 September 2016.

To sample summer long post-fire soil surface temperatures in 2017, I randomly located sample sites within year of burn (YOB) and one-year post burn

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(1PB) wildfire areas and an adjacent unburned (UB) area at PR1 using QGIS. PR1 has a long history of cattle grazing, therefore I conducted all sampling within a single pasture (2665 ha) to homogenize effects of historic grazing intensity and use. No cattle were in the sampled pasture in 2016 or 2017. To measure soil surface temperature, I placed iButtons 0.5 cm under the soil surface on 15 June

2017 to coincide with the summer solstice (20 June 2017). I programed iButtons to record temperature once an hour until the memory filled (2,637 hours). A red painted wooden stake was placed 15 cm north and a red painted washer was placed 15 cm to the south of the iButton’s location to help relocate the iButton. I deployed 14 iButtons in YOB and UB treatments, and 9 iButtons in the 1PB treatment. I deployed fewer iButtons in 1PB area because I were interested in soil surface temperature difference between YOB and UB areas, but I had the opportunity to sample one-year post fire. IButtons stopped recording due to full memory on 9 September 2017 and were collected on 3 October 2017. I also measured vegetation cover (percent bare soil, litter, live vegetation, and dead vegetation) within a 1 m-2 quadrat at each sampling site on 15 June 2017. I obtained weather data from the West Texas Mesonet’s Sundown station located 5 km west of my study site (33° 23' 21’’, N, 102° 36' 36’’ W)

Analysis

To examine whether iButtons could provide an accurate reading of soil surface temperature, I compared measurements between iButtons, soil surface temperature probe, and thermal infrared gun. I averaged the top 4 maximum measurements from each iButton and averaged the 4 soil surface temperature probe and thermal infrared gun measurements on 9 September 2016. To

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Texas Tech University, Britt Smith, May 2018 compare iButton measurements against soil temperature probe and thermal infrared gun, I conducted an analysis of variance with Tukey’s honestly significant difference test.

For PR1, vegetation cover was compared between year of burn, one-year post burn, and unburned using an ANOVA with Tukey’s honestly significant difference test. I examined vegetation cover measurements for skewness using histograms and all variables were normally distributed. To examine post-fire soil surface temperatures, I modeled hourly temperature and mean daily variance across the summer season using measurements gathered from randomly distributed iButtons. Due to wind-driven soil erosion in the year of burn and one- year post burn treatments, some iButtons became fully exposed to solar radiation, which caused the recorded temperature to dramatically increase during the day. Exposed iButtons were removed from the analysis; therefore, analyses contained 7, 5, and 14 iButtons for YOB, 1PB, and UB, respectively. To model hourly soil surface temperature across treatments, conducted a repeated measures harmonic regression using Fourier terms to model the periodic seasonality of the data (Legendre and Legendre 1998). Fixed variables in my model were selected a priori and included hour, Fourier terms ( cos( 2 ∗ 휋 ∗

ℎ표푢푟 ∗ 24−1) and sin( 2 ∗ 휋 ∗ ℎ표푢푟 ∗ 24−1)), treatment, Julian date, solar radiation

(W * 푚−2), and ambient air temperature (measured at 2 m). My model was additive but also included an interaction of hour and treatment. I included iButton as a random variable to account for differences between individual iButtons. Because time-series data are serially correlated, I used a first-order correlation structure (corAR1) to account for autocorrelation in equally distanced

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Texas Tech University, Britt Smith, May 2018 time series. My model was fitted using maximum likelihood. I built my model using the linear mixed effects (LME) function in the packages NLME (Pinheiro et al. 2013) in the program R (R Core Team 2018). To examine model fit, standard

R2 formulation is problematic for mixed models due to the combination of random and fixed effects, I calculated marginal and conditional R2 using the function r.squaredGLMM from the MuMIn package (Bartoń 2009) in the program R. Mean daily variance was also examined using an additive mixed effects model fitted using maximum likelihood with fixed variables including

Julian date, treatment, solar radiation, and ambient air temperature. I included iButton as a random variable and accounted for autocorrelation of the data with a first-order correlation structure (corAR1). I also calculated marginal and conditional R2 values to examine model fit.

Results

Pilot Study

At LLL, I found a significant difference between the three methods used to measure soil surface temperature in unburned (f = 6.173, P = 0.005) and recently burned (f = 19.46, P > 0.001) areas. In the unburned treatment, results of the

Tukey’s HSD showed a significant difference between iButtons and soil temperature probe (P = 0.003), and no significant difference between iButton and thermal infrared gun (P = 0.42) and soil temperature probe and thermal infrared gun (P = 0.07, Fig. 6.2). In recently burned areas, the Tukey’s HSD results showed a significant difference between iButton and thermal infrared gun

(P < 0.001) and soil temperature probe and thermal infrared gun (P < 0.001),

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Texas Tech University, Britt Smith, May 2018 and no significant difference between iButton and soil temperature probe (P =

0.55, Fig. 6.2).

Shortgrass Summer Soil Surface Temperature

Vegetation analyses from PR1 showed significantly greater percent bare soil in YOB compared to 1PB and UB sites (Fig 6.3). Percent litter and dead vegetation was greatest in UB compared to YOB and 1PB (Fig 6.3). Percent live vegetation was greatest in 1PB followed by YOB and UB (Fig 6.3). Mean ambient air temperature across the study was 24.2C with a maximum temperature of 42.5 occurring on 17 June 2017 and minimum of 26.2 C on 8 August 2017. Maximum solar radiation ranged from 1084 (W * m-2) on 9 August 2017 to 747 (W * m-2) on

5 September 2017. Total rainfall during the sampling period was 6.94 mm. Mean maximum relative humidity (15 m) during the study was 88.8% and mean minimum was 31.9. Results from modeled hourly soil surface temperatures showed a significant influence of all predictor variables except for differences between YOB and 1PB treatments (Table 6.1, Fig. 6.5). The marginal and conditional R2 were 0.73 and 0.74, respectively. Modeled results for mean variance per day showed a significant influence of predictor variables except for

Julian date (P = 0.133, Table 6.2, Fig. 6.6). The marginal and conditional R2 were

0.38 and 0.45, respectively.

Discussion

In the pilot study at LLL, I found a significant difference in soil surface temperature in burned areas between iButtons and thermal infrared gun. In the burned treatment, I saw a difference in temperature measurements between the

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Texas Tech University, Britt Smith, May 2018 thermal infrared gun and soil temperature probe. In the unburned treatment, I saw a significant difference between iButtons and the soil surface temperature probe. Though I saw significant differences between iButtons and at least one of the other method in each of the treatments, it appears that iButtons can be used to accurately measure soil surface temperature when place slightly below the soil surface. An observation I made in the field is that the values of the infrared temperature gun can fluctuate drastically when trying take a measurement.

Modeled soil surface temperature shows a significant influence of treatment and the interaction of time and treatment. While the model does not suggest a difference between YOB and 1PB, I did observe greater soil surface temperatures in the burned treatments compared to unburned. The marginal and conditional R2 values for the soil surface temperature model suggest a good fit to the data. Modeled mean daily variance of soil temperature measurements also shows a significant influence of treatment with YOB having the greatest daily variation in soil surface temperatures followed by 1PB and UB. Further, the interaction between time and treatment showed a significant interaction. Time in conjunction with YOB showed an increase in soil surface temperature compared to 1PB, while time and UB showed a decrease compared to 1PB. The variance model had marginal and conditional R2 suggesting a reasonably good fit of the model to the data. Other sources to explain the remaining random noise of the model could include evapotranspiration, wind speeds, and slight differences in iButton depth.

With iButtons being a relatively inexpensive, long-term data logger of temperature, I was able to confirm that soil surface temperatures in recently

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IButtons can be a useful tool for researchers examining the thermal environment’s influence on organisms. IButtons can log soil surface temperature for extended periods of time with minimal effort of establishing measurement sites. Using iButtons, I found that soil surface temperatures in burned shortgrass rangelands experience greater maximum daytime temperatures, lower nighttime minimum temperatures, and greater variance throughout the day compared to unburned rangelands. This can help inform research and help land managers to consider the thermal environment of burned areas when managing shortgrass rangelands for wildlife, livestock, and vegetation.

Literature Cited

Anderson, R.C., 1973. The use of fire as a management tool on the Curtis Prairie. Tall Timbers Fire Ecology Conference Proceedings 12, 23–35.

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Barton, K., 2018. MuMIn: multi-model inference. R package version 1.40.4.

Call, C.A., Roundy, B.A., 1991. Perspectives and processes in revegetation of arid and semiarid rangelands. Journal of Range Management 44, 543–549.

Carroll, J.M., Davis, C.A., Elmore, R.D., Fuhlendorf, S.D., 2015. A ground-nesting galliform’s response to thermal heterogeneity: implications for ground- dwelling birds. PLOS ONE 10, e0143676.

Chappell, M.A., Whitman, D.W., 1990. Grasshopper thermoregulation, in: Chapman, R.F., Joern, A. (Eds.), Biology of grasshoppers. John Wiley & Sons, New York, NY.

Grau-Andrés, R., Davies, G.M., Waldron, S., Scott, E.M., Gray, A., 2017. Leaving moss and litter layers undisturbed reduces the short-term environmental consequences of heathland managed burns. Journal of Environmental Management 204, 102–110.

Legendre, P., Legendre, L., 1998. Numerical ecology. Second English edition. Elsevier Science, Amsterdam, NL.

Limb, R.F., Fuhlendorf, S.D., Townsend, D.E., 2009. Heterogeneity of thermal extremes: driven by disturbance or inherent in the landscape. Environmental Management 43, 100–106.

Moses, M.R., Frey, J.K., Roemer, G.W., 2012. Elevated surface temperature depresses survival of banner-tailed kangaroo rats: will climate change cook a desert icon? Oecologia 168, 257–268.

NOAA-NCDC, 2018. Climate Data Online. https://www.ncdc.noaa.gov/cdo-web/ (accessed 11 January 2018).

Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., 2013. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-131.

QGIS Development Team (2017). QGIS Geographic Information System. Opensource Geospatial Foundation Project. http://qgis.osgeo.org

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R Core Team, 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Rhodes, D.H., Richmond, M.E., 1985. Influence of soil texture, moisture and temperature on nest-site selection and burrowing by the pine vole, Microtus pinetorum. The American Midland Naturalist 113, 102–108.

Sartorius, S.S., do Amaral, J.P.., Durtsche, R.D., Deen, C.M., Lutterschmidt, W.I., 2002. Thermoregulatory accuracy, precision, and effectiveness in two sand-dwelling lizards under mild environmental conditions. Canadian Journal of Zoology 80, 1966–1976.

Sharrow, S.H., Wright, H.A., 1977. Effects of fire, ash, and litter on soil nitrate, temperature, moisture and tobosagrass production in the rolling plains. Journal of Range Management 266–270.

Soil Survey Staff. 2018. Web Soil Survey. Available online at the following link: https://websoilsurvey.sc.egov.usda.gov/. (accessed 11 January 2018).

Tuf, I.H., Dedek, P., Veselý, M., 2012. Does the diurnal activity pattern of carabid beetles depend on season, ground temperature and habitat? Archives of Biological Sciences 64, 721–732.

Vermeire, L.T., Wester, D.B., Mitchell, R.B., Fuhlendorf, S.D., 2005. Fire and grazing effects on wind erosion, soil water content, and soil temperature. Journal of Environment Quality 34, 1559–1565.

Vermeire, L.T., Crowder, J.L., Wester, D.B., 2011. Plant community and soil environment response to summer fire in the northern Great Plains. Rangeland Ecology & Management 64, 37–46.

Zhou, X., Talley, M., Luo, Y., 2009. Biomass, litter, and soil respiration along a precipitation gradient in southern Great Plains, USA. Ecosystems 12, 1369–1380.

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Tables and Figures

Table 6.1 Hourly soil surface temperature model predictor variable coefficients and fit. Variable β s.e. d.f. t-value P Intercept 14.34725 0.509103 52990 28.18 <0.001 Cos -0.35388 0.070229 52990 -5.04 <0.001 Sin -2.26417 0.086803 52990 -26.08 <0.001 Hour 0.000462 6.44E-05 52990 7.17 <0.001 YOB -0.11648 0.08149 52990 -1.43 0.1529 UB 1.211886 0.066044 52990 18.35 <0.001 Solar radiation 0.005212 0.00017 52990 30.60 <0.001 Ambient Air Temp 0.726158 0.009892 52990 73.41 <0.001 Julian Date -0.02145 0.001364 52990 -15.73 <0.001 Hour x YOB 0.000544 0.00006 52990 9.07 <0.001 Hour x UB -0.00206 4.89E-05 52990 -42.11 <0.001

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Table 6.2 Mean daily soil surface temperature variance model predictor variable coefficients and fit. Variable β s.e. d.f. t-value P Intercept -142.1 23.62 2191 -6.02 <0.001 Julian Date 0.099 0.066 2191 1.50 0.134 YOB 4.872 1.999 2191 2.44 0.015 UB -46.87 1.6 2191 -29.30 <0.001 Solar Radiation 0.154 0.026 2191 5.87 <0.001 Ambient Air Temp 6.689 0.538 2191 12.43 <0.001

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Figure 6.1 2017 false color image of sample sites located at PR1. Wildfire boundaries outlined in orange. Location of iButtons indicated by sample points with colored circles. Sample pasture indicated by red polygon.

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Figure 6.2 Mean ± SE soil surface temperature methods between burned and unburned areas at Lubbock Lake Landmark. Methods include iButton, thermal infrared gun (IR Gun), and soil temperature probe. Different letters denote significance differences.

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Texas Tech University, Britt Smith, May 2018

Figure 6.3 Mean ± SE percent vegetation differences between year of burn (YOB), one year post burn (1YPB), and unburned wildfire treatments. Different letters denote significance differences.

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Texas Tech University, Britt Smith, May 2018

Figure 6.4 Mean ± SE iButton daily soil surface temperature by Julian date in year of burn (YOB), one year post burn (1YPB), and unburned wildfire treatments. Points have been offset to improve interpretability. Mean daily solar radiation are denoted by closed grey circles. Fitted regression lines from model parameters Julian and treatment.

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Texas Tech University, Britt Smith, May 2018

Figure 6.5 Mean ± SE hourly iButton soil surface temperature by hour in year of burn (YOB), one year post burn (1YPB), and unburned wildfire treatments. Points have been offset to improve interpretability. Fitted regression lines from model parameters time, Fourier terms, and burn treatment. Mean hourly ambient temperature is represented by closed grey circles

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Texas Tech University, Britt Smith, May 2018

Figure 6.6 Mean ± SE daily variance of iButton soil surface temperature by Julian date year of burn (YOB), one year post burn (1YPB), and unburned wildfire treatments. Points have been offset to improve interpretability. Mean daily solar radiation represented by closed grey circle.

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Texas Tech University, Britt Smith, May 2018

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